Abstract
With a development of microarray technology, the classification of microarray data has arisen as an important topic over the past decade. From various feature selection methods and classifiers, it is very hard to find a perfect method to classify microarray data due to the incompleteness of algorithms, the defects of data, etc. This paper proposes a sophisticated ensemble of such features and classifiers to obtain high classification performance. Speciated genetic algorithm has been exploited to get the diverse ensembles of features and classifiers in a reasonable time. Experimental results with two well-known datasets indicate that the proposed method finds many good ensembles that are superior to other individual classifiers.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 |
| Pages | 590-597 |
| Number of pages | 8 |
| Publication status | Published - 2004 Sep 13 |
| Event | Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 - Portland, OR, United States Duration: 2004 Jun 19 → 2004 Jun 23 |
Publication series
| Name | Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 |
|---|---|
| Volume | 1 |
Other
| Other | Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 |
|---|---|
| Country | United States |
| City | Portland, OR |
| Period | 04/6/19 → 04/6/23 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
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Speciated GA for optimal ensemble classifiers in DNA microarray classification. / Cho, Sung Bae; Park, Chanho.
Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004. 2004. p. 590-597 (Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004; Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Speciated GA for optimal ensemble classifiers in DNA microarray classification
AU - Cho, Sung Bae
AU - Park, Chanho
PY - 2004/9/13
Y1 - 2004/9/13
N2 - With a development of microarray technology, the classification of microarray data has arisen as an important topic over the past decade. From various feature selection methods and classifiers, it is very hard to find a perfect method to classify microarray data due to the incompleteness of algorithms, the defects of data, etc. This paper proposes a sophisticated ensemble of such features and classifiers to obtain high classification performance. Speciated genetic algorithm has been exploited to get the diverse ensembles of features and classifiers in a reasonable time. Experimental results with two well-known datasets indicate that the proposed method finds many good ensembles that are superior to other individual classifiers.
AB - With a development of microarray technology, the classification of microarray data has arisen as an important topic over the past decade. From various feature selection methods and classifiers, it is very hard to find a perfect method to classify microarray data due to the incompleteness of algorithms, the defects of data, etc. This paper proposes a sophisticated ensemble of such features and classifiers to obtain high classification performance. Speciated genetic algorithm has been exploited to get the diverse ensembles of features and classifiers in a reasonable time. Experimental results with two well-known datasets indicate that the proposed method finds many good ensembles that are superior to other individual classifiers.
UR - http://www.scopus.com/inward/record.url?scp=4344698991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=4344698991&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:4344698991
SN - 0780385152
SN - 9780780385153
T3 - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
SP - 590
EP - 597
BT - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
ER -