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Other Ensemble Approaches

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Recent Advances in Ensembles for Feature Selection

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 147))

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Abstract

This chapter describes several new fields, beside the feature selection preprocessing step (the theme of this book), in which ensembles have been successfully used. First, in Sect. 7.1, we introduce a very brief review of the different application fields in which ensembles have been applied, together with basic levels that are used to produce different ensemble designs, and a sample taxonomy. Then, in Sect. 7.2 basic ideas in ensemble classification design, one of the very first machine learning areas in which the idea of ensembles was applied, are stated. As there are many interesting and reference books in ensemble classification, we focus on describing the latest ideas in classification ensembles that address problems such as classification, stream data, missing data and imbalance data. Afterwards, the first attempts for applying the ensemble paradigm to the relatively new field of quantification are described in Sect. 7.3. In Sect. 7.4 we move to describing ensembles for clustering, another area in which ensembles have been increasingly popular. In Sect. 7.5 an attempt on ensembles for discretization is described and, finally, Sect. 7.6 summarizes and discusses the contents of this chapter.

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  1. Facultad de Informática, Universidade da Coruña, A Coruña, Spain

    Verónica Bolón-Canedo & Amparo Alonso-Betanzos

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  1. Verónica Bolón-Canedo
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  2. Amparo Alonso-Betanzos
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Bolón-Canedo, V., Alonso-Betanzos, A. (2018). Other Ensemble Approaches. In: Recent Advances in Ensembles for Feature Selection. Intelligent Systems Reference Library, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-319-90080-3_7

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