Tracking Recurrent Concepts Using Context

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

doi:10.1007/978-3-642-13529-3_19

Cited by 26 publications

(47 citation statements)

References 15 publications

(22 reference statements)

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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 approach proposed in Gomes et al (2010) context-concept relationships are learnt from the concept history. A model from a previously learnt concept associated with a particular context is reused in situations of recurrence.…”

Section: Recurring Conceptsmentioning

confidence: 99%

“…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%

“…With that recognition task in place, it would be possible for the algorithms to avoid relearning something from scratch that has been already learned (Gama & Kosina, 2009Gomes et al, 2010;Gonçalves & Barros, 2013;Katakis et al, 2010;Widmer & Kubat, 1996;Yang et al, 2005). This same idea has been already explored in Gomes et al (2010), where concepts are able to be saved in a repository. Actually, MM-PRec can be seen as an extension of the MRec system presented in Gomes et al (2010).…”

Section: Motivationmentioning

confidence: 99%

“…If context information were available, it could be used to better understand recurring concept changes. However, only a small number of techniques explore context information when dealing with recurring concept changes (Gama & Kosina, 2014;Gomes, Sousa, & Menasalvas, 2010;Harries et al, 1998;Widmer, 1997).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Data Stream Classificationmentioning

confidence: 99%

Section: Recurring Conceptsmentioning

confidence: 99%

Section: Recurring Conceptsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Capturing recurring concepts using discrete Fourier transform

Sakthithasan

Pears

2016

Concurrency and Computation

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SUMMARYIn this research, we address the problem of capturing recurring concepts in a data stream environment. Recurrence capture enables the reuse of previously learned classifiers without the need for relearning while providing for better accuracy during the concept recurrence interval. We capture concepts by applying the discrete Fourier transform to decision tree classifiers to obtain highly compressed versions of the trees at concept drift points in the stream and store such trees in a repository for future use. In addition, the impact of drift detector in enabling stable performance is also studied with the two drift detectors: ADWIN and SeqDrift2 in recurring concept capturing context. Our empirical results on real world and synthetic data exhibiting varying degrees of recurrence show that the Fourier compressed trees are more robust to noise and are able to capture recurring concepts with higher precision than a meta-learning approach that chooses to reuse classifiers in their originally occurring form. A case study on a flight dataset that closely matches the target data stream environment where concepts recur in similar form in a time critical system is also conducted and the benefits of discrete Fourier transform application is evaluated.

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Mining Recurrent Concepts in Data Streams Using the Discrete Fourier Transform

Sakthithasan

Pears

2014

Data Warehousing and Knowledge Discovery

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In this research we address the problem of capturing recurring concepts in a data stream environment. Recurrence capture enables the re-use of previously learned classifiers without the need for re-learning while providing for better accuracy during the concept recurrence interval. We capture concepts by applying the Discrete Fourier Transform (DFT) to Decision Tree classifiers to obtain highly compressed versions of the trees at concept drift points in the stream and store such trees in a repository for future use. Our empirical results on real world and synthetic data exhibiting varying degrees of recurrence show that the Fourier compressed trees are more robust to noise and are able to capture recurring concepts with higher precision than a meta learning approach that chooses to re-use classifiers in their originally occurring form. The DFT, apart from its use in meta learning, has a number of other desirable properties that make it attractive for mining high speed data streams. This includes the ability to classify directly from the spectra generated, thus eliminating the need for expensive traversal of a tree structure.Our experimental results in section 5 clearly show the accuracy, processing speed and memory advantages of applying the DFT as opposed to the meta learning approach proposed by Gama and Kosina in [4].The rest of the paper is as follows. In section 2 we review work done in the area of capturing recurrences. We describe the basics of applying the DFT to decision trees in section 3. In section 4 we discuss a novel approach to optimizing the computation of the Fourier spectrum from a Decision Tree. Our experimental results are presented in section 5 and we conclude the paper in section 6 where we draw conclusions on the research and discuss some directions for future research.

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Tracking Recurrent Concepts Using Context

Cited by 26 publications

References 15 publications

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

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

Capturing recurring concepts using discrete Fourier transform

Mining Recurrent Concepts in Data Streams Using the Discrete Fourier Transform

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