Neural network ensemble with negatively correlated features for cancer classification

Hong Hee Won, Sung Bae Cho

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features to classify cancer precisely, and systematically evaluate the performance of the proposed method using three benchmark datasets. Experimental results show that the neural network ensemble with negatively correlated features produces the best recognition rate on the three benchmark datasets.

Original languageEnglish
Pages (from-to)1143-1150
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2714
Publication statusPublished - 2003 Dec 1

Fingerprint

Neural Network Ensemble
Cancer Classification
Cancer
Classifiers
Neural networks
Microarrays
Classifier
Benchmarking
DNA Microarray
Neoplasms
Microarray Data
Oligonucleotide Array Sequence Analysis
DNA
Benchmark
Classifier Ensemble
Ensemble Classifier
Correlated Errors
Microarray
Ensemble
Classify

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

@article{0d2dfc50674c4dcdb4c55ae0a5af9e30,
title = "Neural network ensemble with negatively correlated features for cancer classification",
abstract = "The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features to classify cancer precisely, and systematically evaluate the performance of the proposed method using three benchmark datasets. Experimental results show that the neural network ensemble with negatively correlated features produces the best recognition rate on the three benchmark datasets.",
author = "Won, {Hong Hee} and Cho, {Sung Bae}",
year = "2003",
month = "12",
day = "1",
language = "English",
volume = "2714",
pages = "1143--1150",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",

}

TY - JOUR

T1 - Neural network ensemble with negatively correlated features for cancer classification

AU - Won, Hong Hee

AU - Cho, Sung Bae

PY - 2003/12/1

Y1 - 2003/12/1

N2 - The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features to classify cancer precisely, and systematically evaluate the performance of the proposed method using three benchmark datasets. Experimental results show that the neural network ensemble with negatively correlated features produces the best recognition rate on the three benchmark datasets.

AB - The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features to classify cancer precisely, and systematically evaluate the performance of the proposed method using three benchmark datasets. Experimental results show that the neural network ensemble with negatively correlated features produces the best recognition rate on the three benchmark datasets.

UR - http://www.scopus.com/inward/record.url?scp=35048878442&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35048878442&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:35048878442

VL - 2714

SP - 1143

EP - 1150

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -