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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2015: Intelligent Information and Database Systems pp 526–535Cite as

Data Classification with Ensembles of One-Class Support Vector Machines and Sparse Nonnegative Matrix Factorization

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9011))

Abstract

The paper presents a method for data classification with ensemble of one-class classifiers based on data segmentation. Each data class is partitioned with the nonnegative matrix factorization (NMF) algorithm with sparse constraints. It allows splitting of the input data into compact and consistent data clusters with automatic determination of a number of clusters. Data partitions are fed to an ensemble composed of a number of one-class support vector machine (SVM) classifiers. The proposed method shows high accuracy and fast classification.

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Author information

Authors and Affiliations

  1. AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Bogusław Cyganek

  2. Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Bartosz Krawczyk

Authors
  1. Bogusław Cyganek
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  2. Bartosz Krawczyk
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Corresponding author

Correspondence to Bogusław Cyganek .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh city, Vietnam

    Ngoc Thanh Nguyen

  2. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

  3. Bina Nusantara University, Jakarta, Indonesia

    Raymond Kosala

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© 2015 Springer International Publishing Switzerland

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Cyganek, B., Krawczyk, B. (2015). Data Classification with Ensembles of One-Class Support Vector Machines and Sparse Nonnegative Matrix Factorization. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9011. Springer, Cham. https://doi.org/10.1007/978-3-319-15702-3_51

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  • DOI: https://doi.org/10.1007/978-3-319-15702-3_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15701-6

  • Online ISBN: 978-3-319-15702-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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