loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Yusuke Sato ; Kazuyuki Narisawa and Ayumi Shinohara

Affiliation: Tohoku University, Japan

ISBN: 978-989-758-048-2

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.

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.229.151.87

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, 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 - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={327-334},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005130103270334},
isbn={978-989-758-048-2},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - A Simple Classification Method for Class Imbalanced Data using the Kernel Mean
SN - 978-989-758-048-2
AU - Sato, Y.
AU - Narisawa, K.
AU - Shinohara, A.
PY - 2014
SP - 327
EP - 334
DO - 10.5220/0005130103270334

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.