Adaptive Mechanisms for Classification Problems with Drifting Data

Sahel, Zoheir; Bouchachia, Abdelhamid; Gabrys, Bogdan; Rogers, Paul

doi:10.1007/978-3-540-74827-4_53

Cited by 20 publications

(10 citation statements)

References 10 publications

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“…There are many techniques [7], [46] such as instance selection, instance weighting, and ensemble learning. Although there are several research avenues using data-driven techniques, in this paper we use model-driven drift handling techniques [39]. Model-driven drift means using appropriate models that are incremental and able to handle drift.…”

Section: Data Drift Handlingmentioning

confidence: 99%

GT2FC: An Online Growing Interval Type-2 Self-Learning Fuzzy Classifier

Bouchachia

Vanaret

2014

IEEE Trans. Fuzzy Syst.

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Abstract-In the present paper we propose a Growing Type-2 Fuzzy Classifier (GT2FC) for online rule learning from real-time data streams. While in batch rule learning the training data are assumed to be drawn from a stationary distribution, in online rule learning data can dynamically change over time becoming potentially non-stationary. To accommodate dynamic change, GT2FC relies on a new semi-supervised online learning algorithm called 2G2M (Growing Gaussian Mixture Model). In particular, 2G2M is used to generate the type-2 fuzzy membership functions to build the type-2 fuzzy rules. GT2FC is designed to accommodate data online and to reconcile labeled and unlabeled data using selflearning. Moreover. GT2FC maintains low complexity of the rule base using online optimization and feature selection mechanisms.GT2FC is tested on data obtained from an ambient intelligence (AmI) application where the goal is to exploit sensed data to monitor the living space on behalf of the inhabitants. Because sensors are prone to faults and noise, type-2 fuzzy modeling is very suitable for dealing with such an application. Thus, GT2FC offers the advantage of dealing with uncertainty in addition to self-adaptation in an online manner. For illustration purposes, GT2FC is also validated on synthetic and classic UCI data sets. The detailed empirical study shows that GT2FC performs very well under various experimental settings.

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Section: Data Drift Handlingmentioning

confidence: 99%

GT2FC: An Online Growing Interval Type-2 Self-Learning Fuzzy Classifier

Bouchachia

Vanaret

2014

IEEE Trans. Fuzzy Syst.

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“…The above concept is further elaborated and extended in Section 4.4. Similar ideas were also investigated in [3], [22], [31], [176]. Integration of the data reduction with the learning process may require introduction of some adaptation mechanisms as exemplified by the idea of learning classifier systems [31].…”

Section: State-of-the-art Reviewmentioning

confidence: 92%

“…It is referred to as the data streams where the class distribution of the streaming data is imbalanced. When the changes of data properties are observed they are also incorporated with data drift and a dynamic character of data source is observed [35], [176]. This problem is also known as the concept drift [217] and such learning is called as learning drift concept [123] or learning classifiers from the streaming data [237].…”

Section: Learning From Data -The Problem Taxonomiesmentioning

confidence: 99%

Distributed Learning with Data Reduction

Czarnowski

2011

Lecture Notes in Computer Science

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Abstract. The work deals with the distributed machine learning. Distributed learning from data is considered to be an important challenge faced by researchers and practice in the domain of the distributed data mining and distributed knowledge discovery from databases. Currently, learning from data is recognized as one of the most widely investigated paradigms of machine learning. At the same time it is perceived as a difficult and demanding computational problem. Even more complex and still to a large extent open is learning from the distributed data. One of the approaches suitable for learning from the geographically distributed data is to select from the local databases relevant local patterns, called also prototypes. Such prototypes are selected using some specialized data reduction methods. The dissertation contains an overview of the problem of learning classifiers from data, followed by a discussion of the distributed learning. The above includes the problem formulation and the state-of-the-art review. Next, data reduction, approaches, techniques and algorithms are discussed. The central part of the dissertation proposes an agent-based distributed learning framework. The idea is to carry-out data reduction in parallel in separate locations, employing specialized software agents. The process ends when locally selected prototypes are moved to a central site and merged into the global knowledge model. The following part of the work contains the results of an extensive computational experiment aiming at validation of the proposed approach. Finally, conclusions and suggestions for further research are formulated.

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“…In a further study Sahel et al (2007), we investigated these incremental algorithms from the perspective of stability when data drifts. In particular, these algorithms were compared against five static classifiers which are updated using retraining.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy classification in dynamic environments

Bouchachia

2010

Soft Comput

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The persistence and evolution of systems essentially depend on their adaptivity to new situations. As an expression of intelligence, adaptivity is a distinguishing quality of any system that is able to learn and to adjust itself in a flexible manner to new environmental conditions and such ability ensures self-correction over time as new events happen, new input becomes available, or new operational conditions occur. This requires self-monitoring of the performance in an ever-changing environment. The relevance of adaptivity is established in numerous domains and by versatile real-world applications. The present paper presents an incremental fuzzy rule-based system for classification purposes. Relying on fuzzy min-max neural networks, the paper explains how fuzzy rules can be continuously online generated to meet the requirements of non-stationary dynamic environments, where data arrives over long periods of time. The approach proposed to deal with an ambient intelligence application. The simulation results show its effectiveness in dealing with dynamic situations and its performance when compared with existing approaches.

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Adaptive Mechanisms for Classification Problems with Drifting Data

Cited by 20 publications

References 10 publications

GT2FC: An Online Growing Interval Type-2 Self-Learning Fuzzy Classifier

GT2FC: An Online Growing Interval Type-2 Self-Learning Fuzzy Classifier

Distributed Learning with Data Reduction

Fuzzy classification in dynamic environments

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