Browsing by Subject "Data stream"
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Item Open Access Binary transformation method for multi-label stream classification(Association for Computing Machinery, 2022-10-17) Gülcan, Ege Berkay; Ecevit, Işın Su; Can, FazlıData 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.Item Open Access Concept learning using one-class classifiers for implicit drift detection in evolving data streams(Springer, 2021-06) Gözüaçık, Ömer; Can, FazliData stream mining has become an important research area over the past decade due to the increasing amount of data available today. Sources from various domains generate a near-limitless volume of data in temporal order. Such data are referred to as data streams, and are generally nonstationary as the characteristics of data evolves over time. This phe nomenon is called concept drift, and is an issue of great importance in the literature, since it makes models obsolete by decreasing their predictive performance. In the presence of concept drift, it is necessary to adapt to change in data to build more robust and efective classifers. Drift detectors are designed to run jointly with classifcation models, updating them when a signifcant change in data distribution is observed. In this paper, we present an implicit (unsupervised) algorithm called One-Class Drift Detector (OCDD), which uses a one-class learner with a sliding window to detect concept drift. We perform a compre hensive evaluation on mostly recent 17 prevalent concept drift detection methods and an adaptive classifer using 13 datasets. The results show that OCDD outperforms the other methods by producing models with better predictive performance on both real-world and synthetic datasets.Item Open Access Diversity and novelty in information retrieval(ACM, 2013-07-08) Santos, R. L. T.; Castells, P.; Altıngövde, I. S.; Can, FazlıThis tutorial aims to provide a unifying account of current research on diversity and novelty in different IR domains, namely, in the context of search engines, recommender sys- tems, and data streams.Item Open Access Diversity and novelty in web search, recommender systems and data streams(Association for Computing Machinery, 2014-02) Santos, R. L. T.; Castells, P.; Altingovde, I. S.; Can, FazlıThis tutorial aims to provide a unifying account of current research on diversity and novelty in the domains of web search, recommender systems, and data stream processing.Item Embargo Dynamic ensemble diversification and hash-based undersampling for the classification of multi-class imbalanced data streams(2024-07) Abadifard, SoheilThe 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.Item Open Access GOOWE-ML: a novel online stacked ensemble for multi-label classification in data streams(2019-07) Büyükçakır, AlicanAs 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.Item Open Access Less is more: a comprehensive framework for the number of components of ensemble classifiers(IEEE, 2019) Bonab, H.; Can, FazlıThe number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of the existing batch and online ensemble classifiers. There are only a limited number of studies on the ensemble size examining majority voting (MV) and weighted MV (WMV). Almost all of them are designed for batch-mode, hardly addressing online environments. Big data dimensions and resource limitations, in terms of time and memory, make the determination of ensemble size crucial, especially for online environments. For the MV aggregation rule, our framework proves that the more strong components we add to the ensemble, the more accurate predictions we can achieve. For the WMV aggregation rule, our framework proves the existence of an ideal number of components, which is equal to the number of class labels, with the premise that components are completely independent of each other and strong enough. While giving the exact definition for a strong and independent classifier in the context of an ensemble is a challenging task, our proposed geometric framework provides a theoretical explanation of diversity and its impact on the accuracy of predictions. We conduct a series of experimental evaluations to show the practical value of our theorems and existing challenges.Item Open Access A novel online stacked ensemble for multi-label stream classification(ACM, 2018) Büyükçakır, Alican; Bonab, H.; Can, FazlıAs 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 ensemble-based 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 multi-label 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.Item Open Access A theoretical framework on the ideal number of classifiers for online ensembles in data streams(ACM, 2016-10) Bonab, Hamed R.; Can, FazlıA priori determining the ideal number of component classifiers of an ensemble is an important problem. The volume and velocity of big data streams make this even more crucial in terms of prediction accuracies and resource requirements. There is a limited number of studies addressing this problem for batch mode and none for online environments. Our theoretical framework shows that using the same number of independent component classifiers as class labels gives the highest accuracy. We prove the existence of an ideal number of classifiers for an ensemble, using the weighted majority voting aggregation rule. In our experiments, we use two state-of-the-art online ensemble classifiers with six synthetic and six real-world data streams. The violation of providing independent component classifiers for our theoretical framework makes determining the exact ideal number of classifiers nearly impossible. We suggest upper bounds for the number of classifiers that gives the highest accuracy. An important implication of our study is that comparing online ensemble classifiers should be done based on these ideal values, since comparing based on a fixed number of classifiers can be misleading. © 2016 ACM.Item Open Access Unsupervised concept drift detection using sliding windows: two contributions(2020-10) Gözüaçık, ÖmerData stream mining has become an important research area over the past decade due to the increasing amount of data available today. Sources from various domains generate limitless volume of data in temporal order. Such data are referred to as data streams, and generally, they are nonstationary as the characteristics of the data evolve over time. This phenomenon is called concept drift, and it is an issue of great importance in the literature since it makes models outdated and decreases their predictive performance. In the presence of concept drift, adapting the change in data is necessary to have more robust and effective classifiers. Drift detectors are designed to run jointly with the classification models, updating them when a significant change in the data distribution is observed. In this study, we propose two unsupervised concept drift detection methods: D3 and OCDD. In D3, we use a discriminative classifier over a sliding window to monitor the change in the distribution of data. When the old and the new data are separable with the discriminative classifier, a drift is signaled. In OCDD, we use a one-class classifier over a sliding window. We monitor the number of outliers identified in the sliding window. We claim that the number of outliers are the signs of a new concept, and define concept drift detection as the continuous form of anomaly detection. A drift is signaled if the percentage of the outliers are over a pre-determined threshold. We perform a comprehensive evaluation on the latest and the most prevalent concept drift detectors using 13 datasets. The results show that OCDD outperforms the other methods by producing models with significantly better predictive performances on both real-world and synthetic datasets. D3 is on par with the other methods.Item Open Access Unsupervised concept drift detection with a discriminative classifier(Association for Computing Machinery, 2019) Gözüaçık, Ömer; Büyükçakır, Alican; Bonab, H.; Can, FazlıIn data stream mining, one of the biggest challenges is to develop algorithms that deal with the changing data. As data evolve over time, static models become outdated. This phenomenon is called concept drift, and it is investigated extensively in the literature. Detecting and subsequently adapting to concept drifts yield more robust and better performing models. In this study, we present an unsupervised method called D3 which uses a discriminative classifier with a sliding window to detect concept drift by monitoring changes in the feature space. It is a simple method that can be used along with any existing classifier that does not intrinsically have a drift adaptation mechanism. We experiment on the most prevalent concept drift detectors using 8 datasets. The results demonstrate that D3 outperforms the baselines, yielding models with higher performances on both real-world and synthetic datasets.