Impact of Boolean factorization as preprocessing methods for classification of Boolean data

Article

Abstract

We explore a utilization of Boolean matrix factorization for data preprocessing in classification of Boolean data. In our previous work, we demonstrated that preprocessing that consists in replacing the original Boolean attributes by factors, i.e. new Boolean attributes obtained from the original ones by Boolean matrix factorization, can improve classification quality. The aim of this paper is to explore the question of how the various Boolean factorization methods that were proposed in the literature impact the quality of classification. In particular, we compare five factorization methods, present experimental results, and outline issues for future research.

Keywords

Matrix decomposition Factor analysis Formal concept analysis 

Mathematics Subject Classifications (2010)

15A23 03C45 46L36 62H25 65F30 68T30 68W25 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Data Analysis and Modeling Lab (DAMOL), Department of Computer SciencePalacky University, OlomoucOlomoucCzech Republic

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