Learning recurring concepts from data streams with a context-aware ensemble

Gomes, João Bártolo; Menasalvas, Ernestina; Sousa, Pedro

doi:10.1145/1982185.1982403

Cited by 36 publications

(30 citation statements)

References 18 publications

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“…Single Example [Schlimmer and Granger 1986], [Littlestone 1987] [Domingos and Hulten 2000], [Kuncheva and Plumpton 2008], [Kelly et al 1999] [Bouchachia 2011a], [Ikonomovska et al 2011] Multiple Examples Fixed Size [Salganicoff 1997], [Widmer and Kubat 1996], [Syed et al 1999], [Hulten et al 2001], [Lazarescu et al 2004], [Bifet and Gavalda 2006;, [Gomes et al 2011] Variable Size [Maloof and Michalski 1995], [Klinkenberg 2004], [Gama et al 2004], [Zhang et al 2008], …”

Section: Data Managementmentioning

confidence: 99%

“…SPC considers learning as a process, and monitors the evolution of this process. Drift detection methods based on SPC appear in [Klinkenberg and Renz 1998;Lanquillon 2002;Gama et al 2004;Gomes et al 2011;Bouchachia 2011a]. …”

Section: Forgetting Mechanismsmentioning

confidence: 99%

“…Retraining [Street and Kim 2001], [Zeira et al 2004], [Klinkenberg and Joachims 2000] Incremental [Schlimmer and Granger 1986], [Littlestone 1987], [Bifet et al 2009], [Hulten et al 2001], [Polikar et al 2001] Streaming [Gama et al 2006], [Ikonomovska et al 2011] Adaptation Methods Blind [Littlestone 1987], [Maloof and Michalski 2000], [Klinkenberg and Renz 1998] [Chu and Zaniolo 2004], ] Informed [Hulten et al 2001], [Gama et al 2006], [Ikonomovska et al 2011] Model Adaptation Model Specific [Hulten et al 2001], [Gama et al 2006], [Harries et al 1998] Model Independent [Wald 1947], [Gama et al 2004], [Wang et al 2003] [Bifet and Gavalda 2006], ] Model Management Single Model [Hulten et al 2001], [Gama et al 2006], [Ikonomovska et al 2011] Recurrent [Widmer 1997], [Gama and Kosina 2011] Ensemble [Polikar et al 2001], [Street and Kim 2001], [Kolter and Maloof 2005], [Gao et al 2007], [Minku and Yao 2011], [Elwell and Polikar 2011] Recurrent [Yang et al 2006], [Katakis et al 2010], [Gomes et al 2011] Model Dependent. As we previously referred [Klinkenberg and Joachims 2000] recognize and handle concept changes using properties of Support Vector Machines.…”

Section: Learning Modementioning

confidence: 99%

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A survey on concept drift adaptation

et al. 2014

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Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for researchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art.

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Section: Data Managementmentioning

confidence: 99%

Section: Forgetting Mechanismsmentioning

confidence: 99%

Section: Learning Modementioning

confidence: 99%

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A survey on concept drift adaptation

et al. 2014

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“…These concept boundaries are determined when a drift detection method signals a change/drift. To improve Gomes et al (2010), which relies on a single classifier (Naive Bayes) to deal with recurring concepts, the use of ensembles has been proposed in Gomes, Menasalvas, and Sousa (2011). The main difference between this system and the one proposed in this paper is the similarity function, that in our case allows to better fit the equivalence between classification models.…”

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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“…These concept boundaries are determined when a drift detection method signals a change/drift. To improve [16], which relies on a single classifier to deal with recurring concepts, the use of ensembles has been proposed in [25].…”

Section: A Recurring Conceptsmentioning

confidence: 99%

Recurrent drifts: applying fuzzy logic to concept similarity function

Abad

Menasalvas

2015

Annals of Computer Science and Information Systems

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Abstract-Recurrent drift, as a specific type of concept drift, is characterised by the appearance of previously seen concepts. Therefore, in those cases the learning process could be saved or at least minimized by applying an already trained classification model. In this paper we propose Fuzzy-Rec, a framework that is able to deal with recurrent concept drifts by means of a repository of classification models and a similarity function.Fuzzy logic is used in the framework to implement the similarity function needed to compare different classification models. This is a crucial aspect when dealing with drift recurrence, as long as some measure must be implemented to determine which model better fits a previously seen context. As it can be seen in the experimentation results of this paper, this fuzzy similarity function provides excellent results both in synthetic and real datasets. As a conclusion, we can state that the introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, saving computational resources by applying not just equal models, but also similar ones.

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Learning recurring concepts from data streams with a context-aware ensemble

Cited by 36 publications

References 18 publications

A survey on concept drift adaptation

A survey on concept drift adaptation

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

Recurrent drifts: applying fuzzy logic to concept similarity function

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