Gene expression classification using optimal feature/classifier ensemble with negative correlation

Jungwon Ryu; Sung Bae Cho

Gene expression classification using optimal feature/classifier ensemble with negative correlation

Jungwon Ryu, Sung Bae Cho

College of Computing

Research output: Contribution to conference › Paper › peer-review

19 Citations (Scopus)

Abstract

In order to predict the cancer class of patients, we have illustrated a classification framework that combines sets of classifiers trained with independent two features. Experimental results show that the feature sets that have negative or non-positive correlations produces very high recognition result.

Original language	English
Pages	198-203
Number of pages	6
Publication status	Published - 2002
Event	2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: 2002 May 12 → 2002 May 17

Other

Other	2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/Territory	United States
City	Honolulu, HI
Period	02/5/12 → 02/5/17

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "Gene expression classification using optimal feature/classifier ensemble with negative correlation",

abstract = "In order to predict the cancer class of patients, we have illustrated a classification framework that combines sets of classifiers trained with independent two features. Experimental results show that the feature sets that have negative or non-positive correlations produces very high recognition result.",

author = "Jungwon Ryu and Cho, {Sung Bae}",

year = "2002",

language = "English",

pages = "198--203",

note = "2002 International Joint Conference on Neural Networks (IJCNN '02) ; Conference date: 12-05-2002 Through 17-05-2002",

}

TY - CONF

T1 - Gene expression classification using optimal feature/classifier ensemble with negative correlation

AU - Ryu, Jungwon

AU - Cho, Sung Bae

PY - 2002

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N2 - In order to predict the cancer class of patients, we have illustrated a classification framework that combines sets of classifiers trained with independent two features. Experimental results show that the feature sets that have negative or non-positive correlations produces very high recognition result.

AB - In order to predict the cancer class of patients, we have illustrated a classification framework that combines sets of classifiers trained with independent two features. Experimental results show that the feature sets that have negative or non-positive correlations produces very high recognition result.

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M3 - Paper

AN - SCOPUS:0036075617

SP - 198

EP - 203

T2 - 2002 International Joint Conference on Neural Networks (IJCNN '02)

Y2 - 12 May 2002 through 17 May 2002

ER -

Gene expression classification using optimal feature/classifier ensemble with negative correlation

Abstract

Other

All Science Journal Classification (ASJC) codes

UN SDGs

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