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Authors: Yusuke Sato ; Kazuyuki Narisawa and Ayumi Shinohara

Affiliation: Tohoku University, Japan

Keyword(s): Class Imbalanced Learning, Fuzzy Support Vector Machine, Kernel Mean.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Support vector machines (SVMs) are among the most popular classification algorithms. However, whereas SVMs perform efficiently in a class balanced dataset, their performance declines for class imbalanced datasets. The fuzzy SVMfor class imbalance learning (FSVM-CIL) is a variation of the SVMtype algorithm to accommodate class imbalanced datasets. Considering the class imbalance, FSVM-CIL associates a fuzzy membership to each example, which represents the importance of the example for classification. Based on FSVM-CIL, we present a simple but effective method here to calculate fuzzy memberships using the kernel mean. The kernel mean is a useful statistic for consideration of the probability distribution over the feature space. Our proposed method is simpler than preceding methods because it requires adjustment of fewer parameters and operates at reduced computational cost. Experimental results show that our proposed method is promising.

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Paper citation in several formats:
Sato, Y.; Narisawa, K. and Shinohara, A. (2014). A Simple Classification Method for Class Imbalanced Data using the Kernel Mean. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - KDIR; ISBN 978-989-758-048-2; ISSN 2184-3228, SciTePress, pages 327-334. DOI: 10.5220/0005130103270334

@conference{kdir14,
author={Yusuke Sato. and Kazuyuki Narisawa. and Ayumi Shinohara.},
title={A Simple Classification Method for Class Imbalanced Data using the Kernel Mean},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - KDIR},
year={2014},
pages={327-334},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005130103270334},
isbn={978-989-758-048-2},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - KDIR
TI - A Simple Classification Method for Class Imbalanced Data using the Kernel Mean
SN - 978-989-758-048-2
IS - 2184-3228
AU - Sato, Y.
AU - Narisawa, K.
AU - Shinohara, A.
PY - 2014
SP - 327
EP - 334
DO - 10.5220/0005130103270334
PB - SciTePress

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