A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function

Jae Pil Hwang; Seongkeun Park; Euntai Kim

doi:10.1016/j.eswa.2011.01.061

A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function

Jae Pil Hwang, Seongkeun Park, Euntai Kim

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

72 Citations (Scopus)

Abstract

In this paper, a new weighted approach on Lagrangian support vector machine for imbalanced data classification problem is proposed. The weight parameters are embedded in the Lagrangian SVM formulation. The training method for weighted Lagrangian SVM is presented and its convergence is proven. The weighted Lagrangian SVM classifier is tested and compared with some other SVMs using synthetic and real data to show its effectiveness and feasibility.

Original language	English
Pages (from-to)	8580-8585
Number of pages	6
Journal	Expert Systems with Applications
Volume	38
Issue number	7
DOIs	https://doi.org/10.1016/j.eswa.2011.01.061
Publication status	Published - 2011 Jul

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2010-0012631 ).

All Science Journal Classification (ASJC) codes

Engineering(all)
Computer Science Applications
Artificial Intelligence

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10.1016/j.eswa.2011.01.061

Cite this

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abstract = "In this paper, a new weighted approach on Lagrangian support vector machine for imbalanced data classification problem is proposed. The weight parameters are embedded in the Lagrangian SVM formulation. The training method for weighted Lagrangian SVM is presented and its convergence is proven. The weighted Lagrangian SVM classifier is tested and compared with some other SVMs using synthetic and real data to show its effectiveness and feasibility.",

author = "Hwang, {Jae Pil} and Seongkeun Park and Euntai Kim",

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N2 - In this paper, a new weighted approach on Lagrangian support vector machine for imbalanced data classification problem is proposed. The weight parameters are embedded in the Lagrangian SVM formulation. The training method for weighted Lagrangian SVM is presented and its convergence is proven. The weighted Lagrangian SVM classifier is tested and compared with some other SVMs using synthetic and real data to show its effectiveness and feasibility.

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A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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