Browsing by Subject "classification"

Now showing 1 - 8 of 8

Open Access
Benefit maximizing classification using feature intervals
(2002) İkizler, Nazlı
For a long time, classification algorithms have focused on minimizing the quantity of prediction errors by assuming that each possible error has identical consequences. However, in many real-world situations, this assumption is not convenient. For instance, in a medical diagnosis domain, misdiagnosing a sick patient as healthy is much more serious than its opposite. For this reason, there is a great need for new classification methods that can handle asymmetric cost and benefit constraints of classifications. In this thesis, we discuss cost-sensitive classification concepts and propose a new classification algorithm called Benefit Maximization with Feature Intervals (BMFI) that uses the feature projection based knowledge representation. In the framework of BMFI, we introduce five different voting methods that are shown to be effective over different domains. A number of generalization and pruning methodologies based on benefits of classification are implemented and experimented. Empirical evaluation of the methods has shown that BMFI exhibits promising performance results compared to recent wrapper cost-sensitive algorithms, despite the fact that classifier performance is highly dependent on the benefit constraints and class distributions in the domain. In order to evaluate costsensitive classification techniques, we describe a new metric, namely benefit accuracy which computes the relative accuracy of the total benefit obtained with respect to the maximum possible benefit achievable in the domain.
Open Access
GOPred: GO molecular function prediction by combined classifiers
(2010) Saraç Ö.S.; Atalay V.; Cetin-Atalay, R.
Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred). © 2010 Saraç et al.
Open Access
Human face detection and eye location in video using wavelets
(2006) Türkan, Mehmet
Human face detection and eye localization problems have received significant attention during the past several years because of wide range of commercial and law enforcement applications. In this thesis, wavelet domain based human face detection and eye localization algorithms are developed. After determining all possible face candidate regions using color information in a given still image or video frame, each region is filtered by a high-pass filter of a wavelet transform. In this way, edge-highlighted caricature-like representations of candidate regions are obtained. Horizontal, vertical and filter-like edge projections of the candidate regions are used as feature signals for classification with dynamic programming (DP) and support vector machines (SVMs). It turns out that the proposed feature extraction method provides good detection rates with SVM based classifiers. Furthermore, the positions of eyes can be localized successfully using horizontal projections and profiles of horizontal- and vertical-crop edge image regions. After an approximate horizontal level detection, each eye is first localized horizontally using horizontal projections of associated edge regions. Horizontal edge profiles are then calculated on the estimated horizontal levels. After determining eye candidate points by pairing up the local maximum point locations in the horizontal profiles with the associated horizontal levels, the verification is also carried out by an SVM based classifier. The localization results show that the proposed algorithm is not affected by both illumination and scale changes.
Open Access
Profiling turkish honeys to determine authenticity using physical and chemical characteristics
(2009) Senyuva H.Z.; Gilbert J.; Silici, S.; Charlton, A.; Dal, C.; Gürel, N.; Cimen, D.
Seventy authentic honey samples of 9 different floral types (rhododendron, chestnut, honeydew, Anzer (thymus spp.), eucalyptus, gossypium, citrus, sunflower, and multifloral) from 15 different geographical regions of Turkey were analyzed for their chemical composition and for indicators of botanical and geographical origin. The profiles of free amino acids, oligosaccharides, and volatile components together with water activity were determined to characterize chemical composition. The microscopic analysis of honey sediment (mellissopalynology) was carried out to identify and count the pollen to provide qualitative indicators to confirm botanical origin. Statistical analysis was undertaken using a bespoke toolbox for Matlab called Metabolab. Discriminant analysis was undertaken using partial least-squares (PLS) regression followed by linear discriminant analysis (LDA). Four data models were constructed and validated. Model 1 used 51 variables to predict the floral origin of the honey samples. This model was also used to identify the top 5 variable important of projection (VIP) scores, selecting those variables that most significantly affected the PLS-LDA calculation. These data related to the phthalic acid, 2-methylheptanoic acid, raffinose, maltose, and sucrose. Data from these compounds were remodeled using PLS-LDA. Model 2 used only the volatiles data, model 3 the sugars data, and model 4 the amino acids data. The combined data set allowed the floral origin of Turkish honey to be accurately predicted and thus provides a useful tool for authentication purposes. However, using variable selection techniques a smaller subset of analytes have been identified that have the capability of classifying Turkish honey according to floral type with a similar level of accuracy. © 2009 American Chemical Society.
Open Access
Pyroelectric infrared (PIR) sensor based event detection
(2009) Soyer, Emin Birey
Pyroelectric Infra-red (PIR) sensors have been extensively used in indoor and outdoor applications as they are low cost, easy to use and widely available. PIR sensors respond to IR radiating objects moving in its viewing range. The current sensors give an output of logical one when they detect a hot object’s motion and a logical zero when there is no moving hot object. In this method, only moving objects can be detected and the rate of false alarm is high. New types of PIR sensors are more sophisticated and more capable. They have a lower false alarm ratio compared to classical ones. Although they can distinguish pets and humans, again they can only be used for detection of hot object motions due to the limitations caused by the usage of the simple comparator structure inside. This structure is unalterable, not flexible for development, and not suitable for implementing algorithms. A new approach is developed to use PIR sensors by modifying the sensor circuitry. Instead of directly using the output of a classical PIR sensor, an analog signal is extracted from the PIR output and it is sampled. As a result, intelligent signal processing algorithms can be developed using the discrete-time sensor signal. In this way, it is possible to develop human, pet and flame detection methods. It is also possible to find the direction of moving objects and estimate their distances from the sensor. Furthermore, the path of a moving target can be estimated using a PIR sensor array. We focus on object and event classification using sampled PIR sensor signals. Pet, human and flame detection methods are comparatively investigated. Different human motion events are modeled and classifed using Hidden Markov Models (HMM) and Conditional Gaussian Mixture Models (CGMMs). The sampled data is wavelet transformed for feature extraction and then fed into HMMs for analysis. The final decision is reached according to the Markov Model producing the highest probability. Experimental results demonstrate the reliability of the proposed HMM based decision and event classification algorithm.
Open Access
Recognition and classification of human activities using wearable sensors
(2012) Yurtman, Aras
We address the problem of detecting and classifying human activities using two different types of wearable sensors. In the first part of the thesis, a comparative study on the different techniques of classifying human activities using tag-based radio-frequency (RF) localization is provided. Position data of multiple RF tags worn on the human body are acquired asynchronously and non-uniformly. Curves fitted to the data are re-sampled uniformly and then segmented. The effect of varying the relevant system parameters on the system accuracy is investigated. Various curve-fitting, segmentation, and classification techniques are compared and the combination resulting in the best performance is presented. The classifiers are validated through the use of two different cross-validation methods. For the complete classification problem with 11 classes, the proposed system demonstrates an average classification error of 8.67% and 21.30% for 5-fold and subject-based leave-one-out (L1O) cross validation, respectively. When the number of classes is reduced to five by omitting the transition classes, these errors become 1.12% and 6.52%. The system demonstrates acceptable classification performance despite that tag-based RF localization does not provide very accurate position measurements. In the second part, data acquired from five sensory units worn on the human body, each containing a tri-axial accelerometer, a gyroscope, and a magnetometer, during 19 different human activities are used to calculate inter-subject and interactivity variations in the data with different methods. Absolute, Euclidean, and dynamic time-warping (DTW) distances are used to assess the similarity of the signals. The comparisons are made using time-domain data and feature vectors. Different normalization methods are used and compared. The “best” subject is defined and identified according to his/her average distance to the other subjects.Based on one of the similarity criteria proposed here, an autonomous system that detects and evaluates physical therapy exercises using inertial sensors and magnetometers is developed. An algorithm that detects all the occurrences of one or more template signals (exercise movements) in a long signal (physical therapy session) while allowing some distortion is proposed based on DTW. The algorithm classifies the executions in one of the exercises and evaluates them as correct/incorrect, identifying the error type if there is any. To evaluate the performance of the algorithm in physical therapy, a dataset consisting of one template execution and ten test executions of each of the three execution types of eight exercise movements performed by five subjects is recorded, having totally 120 and 1,200 exercise executions in the training and test sets, respectively, as well as many idle time intervals in the test signals. The proposed algorithm detects 1,125 executions in the whole test set. 8.58% of the executions are missed and 4.91% of the idle intervals are incorrectly detected as an execution. The accuracy is 93.46% for exercise classification and 88.65% for both exercise and execution type classification. The proposed system may be used to both estimate the intensity of the physical therapy session and evaluate the executions to provide feedback to the patient and the specialist.
Open Access
Recursion operators and classification of integrable nonlinear equations
(2012) Bilen, Ergün
Recursion operators, if they exist, of nonlinear partial differential equations map symmetries to symmetries of these equations. It is this property that the integrable nonlinear partial differential equations possess infinitely many symmetries. In this work we studied two other properties of recursion operators. We shall use the recursion operators as Lax operators in the Gelfand-Dikii formalism and to classify certain integrable equations.
Open Access
Understanding human motion : recognition and retrieval of human activities
(2008) İkizler, Nazlı
Within the ever-growing video archives is a vast amount of interesting information regarding human action/activities. In this thesis, we approach the problem of extracting this information and understanding human motion from a computer vision perspective. We propose solutions for two distinct scenarios, ordered from simple to complex. In the first scenario, we deal with the problem of single action recognition in relatively simple settings. We believe that human pose encapsulates many useful clues for recognizing the ongoing action, and we can represent this shape information for 2D single actions in very compact forms, before going into details of complex modeling. We show that high-accuracy single human action recognition is possible 1) using spatial oriented histograms of rectangular regions when the silhouette is extractable, 2) using the distribution of boundary-fitted lines when the silhouette information is missing. We demonstrate that, inside videos, we can further improve recognition accuracy by means of adding local and global motion information. We also show that within a discriminative framework, shape information is quite useful even in the case of human action recognition in still images. Our second scenario involves recognition and retrieval of complex human activities within more complicated settings, like the presence of changing background and viewpoints. We describe a method of representing human activities in 3D that allows a collection of motions to be queried without examples, using a simple and effective query language. Our approach is based on units of activity at segments of the body, that can be composed across time and across the body to produce complex queries. The presence of search units is inferred automatically by tracking the body, lifting the tracks to 3D and comparing to models trained using motion capture data. Our models of short time scale limb behaviour are built using labelled motion capture set. Our query language makes use of finite state automata and requires simple text encoding and no visual examples. We show results for a large range of queries applied to a collection of complex motion and activity. We compare with discriminative methods applied to tracker data; our method offers significantly improved performance. We show experimental evidence that our method is robust to view direction and is unaffected by some important changes of clothing.