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

Abstract

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.

A survey of active and passive concept drift handling methods

“…In some cases, when the previous concept reappears after a period of time, it will be considered as recurring concept drift. The recurrence of concepts may be periodic or aperiodic 40 . The existing research methods for dealing with recurring concept drift can be divided into two categories.…”

Learning in the presence of concept recurrence in data stream clustering

“…After FLORA, we can see many algorithms which address the idea of concept recurrence. As stated in [10], these algorithms can be broadly classified into single model approaches [11,13,21,36,37] and ensemble-based approaches [12,16,38].…”

“…To retrieve the model that best represents the current concept, it combines two measurements-the error produced by the model on the new window of samples and information obtained from the concept-context relationship history. A meta-model that can predict concept drift in advance and suggest the best model for reuse is proposed in [21].…”

“…The similarity between concepts has to be measured to identify recurring concepts [12,13,16,21,25] are some papers which discuss recurrent concept detection in data stream mining. But all of them consider learning through classification.…”

“…• If concepts are recurring, will it be possible to predict an upcoming concept? What can be different methods adopted to predict a concept [21,38]? • In data stream clustering scenarios, how the performance or the quality of a concept recurrence algorithm can be measured.…”