Tracking Recurrent Concept Drift in Streaming Data Using Ensemble Classifiers

Ramamurthy, Sreenivasan Ramasamy; Bhatnagar, Raj

doi:10.1109/icmla.2007.109

Cited by 48 publications

(40 citation statements)

References 11 publications

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“…However, such drawbacks are a consequence of learning from real world data. File: ida552.tex; BOKCTP/xhs p. 20 20 In relation to the experiments with memory constraints the results in Fig. 3(b) show that the overall accuracy is similar between the experiments, in the order of 72% correctly classifier records.…”

Section: Electricity Market Datasetmentioning

confidence: 82%

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Tracking Recurrent Concepts Using Context

Gomes

Menasalvas

Sousa

2010

Rough Sets and Current Trends in Computing

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Abstract. The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts.In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost.

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Section: Electricity Market Datasetmentioning

confidence: 82%

“…Ramamurthy and Bhatnagar [20] presented an ensemble approach that exploits recurring concepts, using a global set of classifiers learned from sequential data chunks. If no classifier in the ensemble performs better than the error threshold, a new classifier to represent the current concept is learned and stored.…”

Section: Ensemble Of Classifiersmentioning

confidence: 99%

Tracking Recurrent Concepts Using Context

Gomes

Menasalvas

Sousa

2010

Rough Sets and Current Trends in Computing

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“…New al gorithms have appeared during the last years Gaber et al (2007); Gama et al (2004);Hulten, Spencer, and Domingos (2001); Street and Kim (2001);Tsymbal (2004);Žliobaite˙ (2010); Widmer and Kubat (1996), but some other related challenges have received far less attention. Such is the case of situations where the same concept or a similar one reappears, and a previous model could be reused to en hance the learning process in terms of accuracy and processing time as in the case of Gama and Kosina (2009);Gomes et al (2010); Katakis et al (2010); Ramamurthy and Bhatnagar (2007); Yang et al (2005);2006).…”

Section: Data Stream Classificationmentioning

confidence: 99%

“…• In the work of Ramamurthy and Bhatnagar (2007) the authors present an ensemble approach that exploits concept recurrence, using a global set of classifiers learned from sequential data chunks. If no classifier in the ensemble performs better than the error threshold, a new classifier is learned and stored to represent the current concept.…”

Section: Recurring Conceptsmentioning

confidence: 99%

Predicting recurring concepts on data-streams by means of a meta-model and a fuzzy similarity function

Ángel

Bártolo²,

Ernestina

2016

Expert Systems with Applications

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Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behavior to the volatile underlying data distributions, what has been called concept drift. It is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classifica-tion models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The intro-duction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of the MM-PRec system, that integrates a meta-model mechanism and a fuzzy similarity function. The theoretical proposal of MM-PRec is also validated in this paper by means of different experiments using both synthetic and real datasets.

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“…Ensemble based classification We are focusing on ensemble based classification algorithm. A classifier is said to predict randomly, if the probability of data point x being classified to a class c is equal to c's class distribution in the current data block [10]. …”

Section: Algorithmic Strategiesmentioning

confidence: 99%