A rough-set-based two-class classifier for large imbalanced dataset

Junzo Watada; Lee Chuan Lin; Lei Ding; Mohd Ibrahim Shapiai; Lim Chun Chew; Zuwairie Ibrahim; Lee Wen Jau; Marzuki Khalid

doi:10.1007/978-3-642-14616-9_63

A rough-set-based two-class classifier for large imbalanced dataset

Junzo Watada, Lee Chuan Lin, Lei Ding, Mohd Ibrahim Shapiai, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

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

8 Citations (Scopus)

Abstract

The objective of this paper is to provide a rouch-set-based two-class classifier approach to classifying samples in large and imbalanced dataset. A database has plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Prediction is another form of expanding data analysis. It enables us to establish a data model using existing data and to predict the trend of data in future. In this paper, a method consists of data scaling, rough sets analysis and support vector machine with radial basis function (SVM-RBF), which is used to classify a large and imbalanced data set obtained in semiconductor industry.

Original language	English
Pages (from-to)	641-651
Number of pages	11
Journal	Smart Innovation, Systems and Technologies
Volume	4
DOIs	https://doi.org/10.1007/978-3-642-14616-9_63
Publication status	Published - 2010

Keywords

Imbalanced data
RBF kernel function
Rough sets analysis
SVM classifier

ASJC Scopus subject areas

Computer Science(all)
Decision Sciences(all)

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10.1007/978-3-642-14616-9_63

Cite this

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title = "A rough-set-based two-class classifier for large imbalanced dataset",

abstract = "The objective of this paper is to provide a rouch-set-based two-class classifier approach to classifying samples in large and imbalanced dataset. A database has plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Prediction is another form of expanding data analysis. It enables us to establish a data model using existing data and to predict the trend of data in future. In this paper, a method consists of data scaling, rough sets analysis and support vector machine with radial basis function (SVM-RBF), which is used to classify a large and imbalanced data set obtained in semiconductor industry.",

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T1 - A rough-set-based two-class classifier for large imbalanced dataset

AU - Watada, Junzo

AU - Lin, Lee Chuan

AU - Ding, Lei

AU - Shapiai, Mohd Ibrahim

AU - Chew, Lim Chun

AU - Ibrahim, Zuwairie

AU - Jau, Lee Wen

AU - Khalid, Marzuki

PY - 2010

Y1 - 2010

N2 - The objective of this paper is to provide a rouch-set-based two-class classifier approach to classifying samples in large and imbalanced dataset. A database has plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Prediction is another form of expanding data analysis. It enables us to establish a data model using existing data and to predict the trend of data in future. In this paper, a method consists of data scaling, rough sets analysis and support vector machine with radial basis function (SVM-RBF), which is used to classify a large and imbalanced data set obtained in semiconductor industry.

AB - The objective of this paper is to provide a rouch-set-based two-class classifier approach to classifying samples in large and imbalanced dataset. A database has plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Prediction is another form of expanding data analysis. It enables us to establish a data model using existing data and to predict the trend of data in future. In this paper, a method consists of data scaling, rough sets analysis and support vector machine with radial basis function (SVM-RBF), which is used to classify a large and imbalanced data set obtained in semiconductor industry.

KW - Imbalanced data

KW - RBF kernel function

KW - Rough sets analysis

KW - SVM classifier

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A rough-set-based two-class classifier for large imbalanced dataset

Abstract

Keywords

ASJC Scopus subject areas

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