Browsing by Subject "Classification"

Now showing 1 - 20 of 50

Open Access
A decomposable branch-and-price formulation for optimal classification trees
(2024-07) Yöner, Elif Rana
Construction of Optimal Classification Trees (OCTs) using mixed-integer programs, is a promising approach as it returns a tree with minimum classification error. Yet solving integer programs to optimality is known to be computationally costly, especially as the size of the instance and the depth of the tree grow, calling for efficient solution methods. Our research presents a new, decomposable model which lends itself to efficient solution algorithms such as Branch-and-Price. We model the classification tree using a “patternbased” formulation, deciding which feature should be used to split data at each branching node of each leaf. Our results are promising, illustrating the potential of decomposition in the domain of binary OCTs.
Open Access
Artificial intelligence-based hybrid anomaly detection and clinical decision support techniques for automated detection of cardiovascular diseases and Covid-19
(2023-10) Terzi, Merve Begüm
Coronary artery diseases are the leading cause of death worldwide, and early diagnosis is crucial for timely treatment. To address this, we present a novel automated arti cial intelligence-based hybrid anomaly detection technique com posed of various signal processing, feature extraction, supervised, and unsuper vised machine learning methods. By jointly and simultaneously analyzing 12-lead electrocardiogram (ECG) and cardiac sympathetic nerve activity (CSNA) data, the automated arti cial intelligence-based hybrid anomaly detection technique performs fast, early, and accurate diagnosis of coronary artery diseases. To develop and evaluate the proposed automated arti cial intelligence-based hybrid anomaly detection technique, we utilized the fully labeled STAFF III and PTBD databases, which contain 12-lead wideband raw recordings non invasively acquired from 260 subjects. Using the wideband raw recordings in these databases, we developed a signal processing technique that simultaneously detects the 12-lead ECG and CSNA signals of all subjects. Subsequently, using the pre-processed 12-lead ECG and CSNA signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of coronary artery diseases. Using the extracted discriminative features, we developed a supervised classi cation technique based on arti cial neural networks that simultaneously detects anomalies in the 12-lead ECG and CSNA data. Furthermore, we developed an unsupervised clustering technique based on the Gaussian mixture model and Neyman-Pearson criterion that performs robust detection of the outliers corresponding to coronary artery diseases. By using the automated arti cial intelligence-based hybrid anomaly detection technique, we have demonstrated a signi cant association between the increase in the amplitude of CSNA signal and anomalies in ECG signal during coronary artery diseases. The automated arti cial intelligence-based hybrid anomaly de tection technique performed highly reliable detection of coronary artery diseases with a sensitivity of 98.48%, speci city of 97.73%, accuracy of 98.11%, positive predictive value (PPV) of 97.74%, negative predictive value (NPV) of 98.47%, and F1-score of 98.11%. Hence, the arti cial intelligence-based hybrid anomaly detection technique has superior performance compared to the gold standard diagnostic test ECG in diagnosing coronary artery diseases. Additionally, it out performed other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it signi cantly increases the detec tion performance of coronary artery diseases by taking advantage of the diversity in di erent data types and leveraging their strengths. Furthermore, its perfor mance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or clas sify coronary artery diseases. It also has a very short implementation time, which is highly desirable for real-time detection of coronary artery diseases in clinical practice. The proposed automated arti cial intelligence-based hybrid anomaly detection technique may serve as an e cient decision-support system to increase physicians' success in achieving fast, early, and accurate diagnosis of coronary artery diseases. It may be highly bene cial and valuable, particularly for asymptomatic coronary artery disease patients, for whom the diagnostic information provided by ECG alone is not su cient to reliably diagnose the disease. Hence, it may signi cantly improve patient outcomes, enable timely treatments, and reduce the mortality associated with cardiovascular diseases. Secondly, we propose a new automated arti cial intelligence-based hybrid clinical decision support technique that jointly analyzes reverse transcriptase polymerase chain reaction (RT-PCR) curves, thorax computed tomography im ages, and laboratory data to perform fast and accurate diagnosis of Coronavirus disease 2019 (COVID-19). For this purpose, we retrospectively created the fully labeled Ankara University Faculty of Medicine COVID-19 (AUFM-CoV) database, which contains a wide variety of medical data, including RT-PCR curves, thorax computed tomogra phy images, and laboratory data. The AUFM-CoV is the most comprehensive database that includes thorax computed tomography images of COVID-19 pneu monia (CVP), other viral and bacterial pneumonias (VBP), and parenchymal lung diseases (PLD), all of which present signi cant challenges for di erential diagnosis. We developed a new automated arti cial intelligence-based hybrid clinical de cision support technique, which is an ensemble learning technique consisting of two preprocessing methods, long short-term memory network-based deep learning method, convolutional neural network-based deep learning method, and arti cial neural network-based machine learning method. By jointly analyzing RT-PCR curves, thorax computed tomography images, and laboratory data, the proposed automated arti cial intelligence-based hybrid clinical decision support technique bene ts from the diversity in di erent data types that are critical for the reliable detection of COVID-19 and leverages their strengths. The multi-class classi cation performance results of the proposed convolu tional neural network-based deep learning method on the AUFM-CoV database showed that it achieved highly reliable detection of COVID-19 with a sensitivity of 91.9%, speci city of 92.5%, precision of 80.4%, and F1-score of 86%. There fore, it outperformed thorax computed tomography in terms of the speci city of COVID-19 diagnosis. Moreover, the convolutional neural network-based deep learning method has been shown to very successfully distinguish COVID-19 pneumonia (CVP) from other viral and bacterial pneumonias (VBP) and parenchymal lung diseases (PLD), which exhibit very similar radiological ndings. Therefore, it has great potential to be successfully used in the di erential diagnosis of pulmonary dis eases containing ground-glass opacities. The binary classi cation performance results of the proposed convolutional neural network-based deep learning method showed that it achieved a sensitivity of 91.5%, speci city of 94.8%, precision of 85.6%, and F1-score of 88.4% in diagnosing COVID-19. Hence, it has compara ble sensitivity to thorax computed tomography in diagnosing COVID-19. Additionally, the binary classi cation performance results of the proposed long short-term memory network-based deep learning method on the AUFM-CoV database showed that it performed highly reliable detection of COVID-19 with a sensitivity of 96.6%, speci city of 99.2%, precision of 98.1%, and F1-score of 97.3%. Thus, it outperformed the gold standard RT-PCR test in terms of the sensitivity of COVID-19 diagnosis Furthermore, the multi-class classi cation performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database showed that it diagnosed COVID-19 with a sen sitivity of 66.3%, speci city of 94.9%, precision of 80%, and F1-score of 73%. Hence, it has been shown to very successfully perform the di erential diagnosis of COVID-19 pneumonia (CVP) and other pneumonias. The binary classi cation performance results of the automated arti cial intelligence-based hybrid clinical decision support technique revealed that it diagnosed COVID-19 with a sensi tivity of 90%, speci city of 92.8%, precision of 91.8%, and F1-score of 90.9%. Therefore, it exhibits superior sensitivity and speci city compared to laboratory data in COVID-19 diagnosis. The performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database demon strate its ability to provide highly reliable diagnosis of COVID-19 by jointly ana lyzing RT-PCR data, thorax computed tomography images, and laboratory data. Consequently, it may signi cantly increase the success of physicians in diagnosing COVID-19, assist them in rapidly isolating and treating COVID-19 patients, and reduce their workload in daily clinical practice.
Open Access
Binary transformation method for multi-label stream classification
(Association for Computing Machinery, 2022-10-17) Gülcan, Ege Berkay; Ecevit, Işın Su; Can, Fazlı
Data streams produce extensive data with high throughput from various domains and require copious amounts of computational resources and energy. Many data streams are generated as multi-labeled and classifying this data is computationally demanding. Some of the most well-known methods for Multi-Label Stream Classification are Problem Transformation schemes; however, previous work on this area does not satisfy the efficiency demands of multi-label data streams. In this study, we propose a novel Problem Transformation method for Multi-Label Stream Classification called Binary Transformation, which utilizes regression algorithms by transforming the labels into a continuous value. We compare our method against three of the leading problem transformation methods using eight datasets. Our results show that Binary Transformation achieves statistically similar effectiveness and provides a much higher level of efficiency.
Open Access
BolT: Fused window transformers for fMRI time series analysis
(Elsevier B.V., 2023-05-18) Bedel, Hasan Atakan; Şıvgın, Irmak; Dalmaz, Onat; Ul Hassan Dar, Salman ; Çukur, Tolga
Deep-learning models have enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for contextual representations across diverse time scales. Here, we present BolT, a blood-oxygen-level-dependent transformer model, for analyzing multi-variate fMRI time series. BolT leverages a cascade of transformer encoders equipped with a novel fused window attention mechanism. Encoding is performed on temporally-overlapped windows within the time series to capture local representations. To integrate information temporally, cross-window attention is computed between base tokens in each window and fringe tokens from neighboring windows. To gradually transition from local to global representations, the extent of window overlap and thereby number of fringe tokens are progressively increased across the cascade. Finally, a novel cross-window regularization is employed to align high-level classification features across the time series. Comprehensive experiments on large-scale public datasets demonstrate the superior performance of BolT against state-of-the-art methods. Furthermore, explanatory analyses to identify landmark time points and regions that contribute most significantly to model decisions corroborate prominent neuroscientific findings in the literature.
Open Access
Çağrı merkezi metin madenciliği yaklaşımı
(IEEE, 2017-05) Yiğit, İ. O.; Ateş, A. F.; Güvercin, Mehmet; Ferhatosmanoğlu, Hakan; Gedik, Buğra
Günümüzde çağrı merkezlerindeki görüşme kayıtlarının sesten metne dönüştürülebilmesi görüşme kaydı metinleri üzerinde metin madenciliği yöntemlerinin uygulanmasını mümkün kılmaktadır. Bu çalışma kapsamında görüşme kaydı metinleri kullanarak görüşmenin içeriğinin duygu yönünden (olumlu/olumsuz) değerlendirilmesi, müşteri memnuniyetinin ve müşteri temsilcisi performansının ölçülmesi amaçlanmaktadır. Yapılan çalışmada görüşme kaydı metinlerinden metin madenciliği yöntemleri ile yeni özellikler çıkarılmıştır. Metinlerden elde edilen özelliklerden yararlanılarak sınıflandırma ve regresyon yöntemleriyle görüşme kayıtlarının içeriklerinin değerlendirilmesini sağlayacak tahmin modelleri oluşturulmuştur. Bu çalışma sonucunda ortaya çıkarılan tahmin modellerinin Türk Telekom bünyesindeki çağrı merkezlerinde kullanılması hedeflenmektedir.
Open Access
Can computerized adaptive testing work in students’ admission to higher education programs in Turkey?
(EDAM, 2017-04) Kalender, I.; Berberoglu, G.
Admission into university in Turkey is very competitive and features a number of practical problems regarding not only the test administration process itself, but also concerning the psychometric properties of test scores. Computerized adaptive testing (CAT) is seen as a possible alternative approach to solve these problems. In the first phase of the study, a series of CAT simulations based on real students’ responses to science items were conducted in order to determine which test termination rule produced more comparable results with scores made on the paper and pencil version of the test. An average of 17 items was used to terminate the CAT administration for a reasonable reliability level as opposed to the normal 45 items. Moreover, CAT based science scores not only produced similar correlations when using mathematics subtest scores as an external criterion, but also ranked the students similarly to the paper and pencil test version. In the second phase, a live CAT administration was implemented using an item bank composed of 242 items with a group of students who had previously taken the exam the paper and pencil version of the test. A correlation of .76 was found between the CAT and paper and pencil scores for this group. The results seem to support the CAT version of the subtests as a feasible alternative approach in Turkey’s university admission system.
Restricted
Classification in Art
(1987) DiMaggio, Paul
Open Access
Classification of agricultural kernels using impact acoustic signal processing
(2006) Onaran, İbrahim
The quality is the main factor that directly affects the price for many agricultural produces. The quality depends on different properties of the produce. Most important property is associated with health of consumers. Other properties mostly depend on the type of concerned vegetable. For instance, emptiness is important for hazelnuts while openness is crucial for the pistachio nuts. Therefore, the agricultural produces should be separated according to their quality to maintain the consumers health and increase the price of the produce in international trades. Current approaches are mostly based on invasive chemical analysis of some selected food items or sorting food items according to their color. Although chemical analysis gives the most accurate results, it is impossible to analyze large quantities of food items. The impact sound signal processing can be used to classify these produces according to their quality. These methods are inexpensive, noninvasive and most of all they can be applied in real-time to process large amount of food. Several signal processing methods for extracting impact sound features are proposed to classify the produces according to their quality. These methods are including time and frequency domain methods. Several time and frequency domain methods including Weibull parameters, maximum points and variances in time windows, DFT (Discrete Fourier Transform) coefficients around the maximum spectral points etc. are used to extract the features from the impact sound. In this study, we used hazelnut and wheat kernel impact sounds. The success rate over 90% is achieved for all types produces.
Open Access
Competitive and online piecewise linear classification
(IEEE, 2013) Özkan, Hüseyin; Donmez, M.A.; Pelvan O.S.; Akman, A.; Kozat, Süleyman S.
In this paper, we study the binary classification problem in machine learning and introduce a novel classification algorithm based on the 'Context Tree Weighting Method'. The introduced algorithm incrementally learns a classification model through sequential updates in the course of a given data stream, i.e., each data point is processed only once and forgotten after the classifier is updated, and asymptotically achieves the performance of the best piecewise linear classifiers defined by the 'context tree'. Since the computational complexity is only linear in the depth of the context tree, our algorithm is highly scalable and appropriate for real time processing. We present experimental results on several benchmark data sets and demonstrate that our method provides significant computational improvement both in the test (5 ∼ 35×) and training phases (40 ∼ 1000×), while achieving high classification accuracy in comparison to the SVM with RBF kernel. © 2013 IEEE.
Open Access
Computer network intrusion detection using various classifiers and ensemble learning
(IEEE, 2018) Mirza, Ali H.
In this paper, we execute anomaly detection over the computer networks using various machine learning algorithms. We then combine these algorithms to boost the overall performance. We implement three different types of classifiers, i.e, neural networks, decision trees and logistic regression. We then boost the overall performance of the intrusion detection algorithm using ensemble learning. In ensemble learning, we employ weighted majority voting scheme based on the individual classifier performance. We demonstrate a significant increase in the accuracy through a set of experiments KDD Cup 99 data set for computer network intrusion detection.
Open Access
Convexity and logical analysis of data
(Elsevier, 2000) Ekin, O.; Hammer, P. L.; Kogan, A.
A Boolean function is called k-convex if for any pair x,y of its true points at Hamming distance at most k, every point "between" x and y is also true. Given a set of true points and a set of false points, the central question of Logical Analysis of Data is the study of those Boolean functions whose values agree with those of the given points. In this paper we examine data sets which admit k-convex Boolean extensions. We provide polynomial algorithms for finding a k-convex extension, if any, and for finding the maximum k for which a k-convex extension exists. We study the problem of uniqueness, and provide a polynomial algorithm for checking whether all k-convex extensions agree in a point outside the given data set. We estimate the number of k-convex Boolean functions, and show that for small k this number is doubly exponential. On the other hand, we also show that for large k the class of k-convex Boolean functions is PAC-learnable. (C) 2000 Elsevier Science B.V. All rights reserved.
Open Access
Deep learning with extended exponential linear unit (DELU)
(Springer, 2023-08-16) Çatalbaş, Burak; Morgül, Ömer
Activation functions are crucial parts of artificial neural networks. From the first perceptron created artificially up to today, many functions are proposed. Some of them are currently in common use, such as Rectified Linear Unit (ReLU) and Exponential Linear Unit (ELU) and other ReLU variants. In this article we propose a novel activation function, called ExtendeD Exponential Linear Unit (DELU). After its introduction and presenting its basic properties, by making various simulations with different datasets and architectures, we show that it may perform better than other activation functions in certain cases. While also inheriting most of the good properties of ReLU and ELU, DELU offers an increase of success in comparison with them by slowing the alignment of neurons in early stages of training process. In experiments, DELU performed better than other activation functions in general, for Fashion MNIST, CIFAR-10 and CIFAR-100 classification tasks with different sized Residual Neural Networks (ResNet). Specifically, DELU managed to reduce the error rate by sufficiently high confidence levels in CIFAR datasets in comparison with ReLU and ELU networks. In addition, DELU is compared in an image segmentation example as well. Also, compatibility of DELU is tested with different initializations, and statistical methods are employed to verify these success rates by using Z-score analysis, which may be considered as a different view of success assessment in neural networks.
Open Access
Detection of underdeveloped hazelnuts from fully developed nuts by impact acoustics
(American Society of Agricultural and Biological Engineers, 2006) Onaran, I.; Pearson, T. C.; Yardimci, Y.; Çetin, A. Enis
Shell-to-kernel weight ratio is a vital measurement of quality in hazelnuts as it helps to identify nuts that have underdeveloped kernels. Nuts containing underdeveloped kernels may contain mycotoxin-producing molds, which are linked to cancer and are heavily regulated in international trade. A prototype system was set up to detect underdeveloped hazelnuts by dropping them onto a steel plate and recording the acoustic signal that was generated when a kernel hit the plate. A feature vector comprising line spectral frequencies and time-domain maxima that describes both the time and frequency nature of the impact sound was extracted from each sound signal and used to classify each nut by a support-vector machine. Experimental studies demonstrated accuracies as high as 97% in classifying hazelnuts with underdeveloped kernels.
Open Access
Discrimination between closed-and open-shell (Turkish) pistachio nuts using undecimated wavelet packet transform
(American Society of Agricultural and Biological Engineers, 2008) Ince, N. F.; Goksu, F.; Tewfik, A. H.; Onaran, I.; Çetin, A. Enis; Pearson, T. C.
Due to low consumer acceptance and the possibility of immature kernels, closed-shell pistachio nuts should be separated from open-shell nuts before reaching the consumer. A system using impact acoustics as a means of classifying closed-shell nuts from open-shell nuts has already been shown to be feasible and have better discrimination performance than a mechanical system. The accuracy of an impact acoustics based system is determined by the signal processing and feature extraction procedures. In this article, a new time-frequency plain feature extraction and classification algorithm was developed to discriminate between open- and closed-shell pistachio nuts produced in the Gaziantep region of Turkey. The proposed approach relies on the analysis of the impact acoustics signal of pistachio nuts, which are emitted from their impact with a steel plate after dropping from a certain height. Features are extracted by decomposing the acoustic signals into time and frequency components, using double-tree undecimated wavelet packet transform. The most discriminative features from the dual tree nodes are selected by a wrapper strategy that includes the structural pruning of the double-tree feature dictionary. The proposed approach requires no prior knowledge of the relevant time or frequency content of the acoustic signals. The algorithm used a small number of features and achieved a classification accuracy of 91.7% on the validation data set, while separating the closed shells from the open ones. A previously implemented algorithm, which uses maximum signal amplitude, absolute integration, and gradient features, achieved 82% classification accuracy on the same dataset. The results show that the time-frequency features extracted from impact acoustics can be used successfully for classification of open- and closed-shell Turkish pistachios.
Open Access
Dynamic ensemble diversification and hash-based undersampling for the classification of multi-class imbalanced data streams
(2024-07) Abadifard, Soheil
The classification of imbalanced data streams, which have unequal class distributions, is a key difficulty in machine learning, especially when dealing with multiple classes and concept drift. While binary imbalanced data stream classification tasks have received considerable attention, only a few studies have focused on multi-class imbalanced data streams. Additionally, dealing with the dynamic imbalance ratio is of great importance. This study introduces a novel, robust, and resilient approach to address these challenges by integrating Locality Sensitive Hashing with Random Hyperplane Projections (LSH-RHP) into the Dynamic Ensemble Diversification (DynED) framework. To the best of our knowledge, we present the first application of LSH-RHP for undersampling in the context of imbalanced non-stationary data streams. The proposed method, undersamples majority classes by utilizing LSH-RHP, provides a balanced training set, and improves the ensemble’s prediction accuracy. We conduct comprehensive experiments on 23 real-world and ten semi-synthetic datasets and compare LSHDynED with 15 state-of-the-art methods. The results reveal that LSH-DynED outperforms other approaches in terms of both Kappa and mG-Mean effectiveness measures, demonstrating its capability in dealing with multi-class imbalanced non-stationary data streams. Notably, LSH-DynED performs well in large-scale, high-dimensional datasets with considerable class imbalances and demonstrates adaptation and robustness in real-world circumstances. For the reproducibility of our results, we have made our implementation available on GitHub.
Open Access
Estimating the chance of success and suggestion for treatment in IVF
(2013) Mısırlı, Gizem
In medicine, the chance of success for a treatment is important for decision making for the doctor and the patient. This thesis focuses on the domain of In Vitro Fertilization (IVF), where there are two issues: the first one is the decision on whether or not go with the treatment procedure, the second one is the selection of the proper treatment protocol for the patient. It is important for both the doctor and the couple to have some idea about the chance of success of the treatment after the initial evaluation. If the chance of success is low, the patient couple may decide not to proceed with this stressful and expensive treatment. Once a decision for treatment is made, the next issue for the doctors is the choice of the treatment protocol which is the most suitable for the couple. Our first aim is to develop techniques to estimate the chance of success and determine the factors that affect the success in IVF treatment. So, we employ ranking algorithms to estimate the chance of success. The ranking methods used are RIMARC (Ranking Instances by Maximizing the Area under the ROC Curve), SVMlight (Support Vector Machine Ranking Algorithm) and RIkNN (Ranking Instances using k Nearest Neighbour). All of these three algorithms learn a model to rank the instances based on their score values. RIMARC is a method for ranking instances by maximizing the area under the ROC curve. SVMlight is an implementation of Support Vector Machine for ranking instances. RIkNN is a k Nearest Neighbour (kNN) based algorithm that is developed for ranking instances based on similarity metric. We also used RIwkNN, which is the version of RIkNN where the features are assigned weights by experts in the domain. These algorithms are compared on the basis of the AUC of 10-fold stratified cross-validation. Moreover, these ranking algorithms are modified as a classification algorithm and compared on the basis of the accuracy of 10-fold stratified cross-validation. As a by-product, the RIMARC algorithm learns the factors that affect the success in IVF treatment. It calculates feature weights and creates rules that are in a human readable form and easy to interpret. After a decision for a treatment is made, the second aim is to determine which treatment protocol is the most suitable for the couple. In IVF treatment, many different types of drugs and dosages are used, however, which drug and the dosage are the most suitable for the given patient is not certain. Doctors generally make their decision based on their past experiences and the results of research published all over the world. To the best of our knowledge, there are no methods for learning a model that can be used to suggest the best feature values to increase the chance that the class label to be the desired one. We will refer to such a system as Suggestion System. To help doctors in making decision on the selection of the suitable treatment protocols, we present three suggestion systems that are based on well-known machine learning techniques. We will call the suggestion systems developed as a part of this work as NSNS (Nearest Successful Neighbour Based Suggestion), kNNS (k Nearest Neighbour Based Suggestion) and DTS (Decision Tree Based Suggestion). We also implemented the weighted version of NSNS using feature weights that are produced by the RIMARC algorithm. Moreover, we propose performance metrics for the evaluation of the suggestion algorithms. We introduce four evaluation metrics namely; pessimistic metric (mp), optimistic metric (mo), validated optimistic metric (mvo) and validated pessimistic metric (mvp) to test the correctness of the algorithms. In order to help doctors to utilize developed algorithms, we develop a decision support system, called RAST (Risk Analysis and Suggestion for Treatment). This system is actively being used in the IVF center at Etlik Z¨ubeyde Hanım Woman’s Health and Teaching Hospital.
Open Access
Estimating the chance of success in IVF treatment using a ranking algorithm
(Springer, 2015) Güvenir, H. A.; Misirli, G.; Dilbaz, S.; Ozdegirmenci, O.; Demir, B.; Dilbaz, B.
In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment.
Open Access
Global vs local classification models for multi-sensor data fusion
(ACM, 2018) Pippa, E.; Zacharaki, E. I.; Özdemir, A. T.; Barshan, Billur; Megalooikonomou, V.
The aim of this paper is to investigate feature extraction and fusion of information across a number of sensors in different spatial locations to classify temporal events. Although the common feature-level fusion allows capturing spatial dependencies across sensors, the significant increase of feature vector dimensionality does not allow learning the classification models using a small number of samples usually available in practice. In decision-level fusion on the other hand, sensor-specific classification models are trained and subsequently integrated to reach a combined decision. Recent work has shown that decision-level fusion with a global (common for all sensors) classification model, is more appropriate for generalized events that show a (weak or strong) manifestation across all sensors. Although we can hypothesize that the choice of scheme depends on the event type (generalized vs focal/local), the prior work does not provide enough evidence to guide on the choice of fusion scheme. Thus in this work we aim to compare the three data fusion schemes for classification of generalized and non-generalized events using two case scenarios: (i) classification of paroxysmal events based on EEG patterns and (ii) classification of falls and activities of daily living (ADLs) from multiple sensors. The results support our hypothesis that feature level fusion is more beneficial for the characterization of heterogeneous data (based on an adequate number of samples), while sensor-independent classifiers should be selected in the case of generalized manifestation patterns.
Open Access
GOOWE-ML: a novel online stacked ensemble for multi-label classification in data streams
(2019-07) Büyükçakır, Alican
As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each instance in the data stream is classified into one or more pre-defined sets of labels. Many methods have been proposed to tackle this problem, including but not limited to ensemblebased methods. Some of these ensemble-based methods are specifically designed to work with certain multi-label base classifiers; some others employ online bagging schemes to build their ensembles. In this study, we introduce a novel online and dynamically-weighted stacked ensemble for multi-label classification, called GOOWE-ML, that utilizes spatial modeling to assign optimal weights to its component classifiers. Our model can be used with any existing incremental multilabel classification algorithm as its base classifier. We conduct experiments with 4 GOOWE-ML-based multi-label ensembles and 7 baseline models on 7 real-world datasets from diverse areas of interest. Our experiments show that GOOWE-ML ensembles yield consistently better results in terms of predictive performance in almost all of the datasets, with respect to the other prominent ensemble models.
Open Access
Human visual cortical responses to specular and matte motion flows
(Frontiers Media S. A, 2015) Kam, T.-E.; Mannion, D.J.; Lee, S.-W.; Doerschner, K.; Kersten, D.J.
Determining the compositional properties of surfaces in the environment is an important visual capacity. One such property is specular reflectance, which encompasses the range from matte to shiny surfaces. Visual estimation of specular reflectance can be informed by characteristic motion profiles; a surface with a specular reflectance that is difficult to determine while static can be confidently disambiguated when set in motion. Here, we used fMRI to trace the sensitivity of human visual cortex to such motion cues, both with and without photometric cues to specular reflectance. Participants viewed rotating blob-like objects that were rendered as images (photometric) or dots (kinematic) with either matte-consistent or shiny-consistent specular reflectance profiles. We were unable to identify any areas in low and mid-level human visual cortex that responded preferentially to surface specular reflectance from motion. However, univariate and multivariate analyses identified several visual areas; V1, V2, V3, V3A/B, and hMT+, capable of differentiating shiny from matte surface flows. These results indicate that the machinery for extracting kinematic cues is present in human visual cortex, but the areas involved in integrating such information with the photometric cues necessary for surface specular reflectance remain unclear. © 2015 Kam, Mannion, Lee, Doerschner and Kersten.