Abstract
Recently, demand on the tools to efficiently analyze biological genomic information has been on the rise. In this paper, we attempt to explore the optimal features and classifiers through a comparative study with the most promising feature selection methods and machine learning classifiers. In order to predict the cancer class, the gene information from patient’s marrow expressed by DNA microarray, who has either the acute myeloid leukemia or acute lymphoblastic leukemia. Pearson and Spearman’s correlation, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Backpropagation neural network, self-organizing map, structure adaptive self-organizing map, support vector machine, inductive decision tree and k-nearest neighbor have been used for classification. Experimental results indicate that backpropagation neural network with Pearson’s correlation coefficients is the best method, obtaining 97.1% of recognition rate on the test data.
| Original language | English |
|---|---|
| Title of host publication | Advances in Soft Computing - AFSS 2002 - 2002 AFSS International Conference on Fuzzy Systems, Proceedings |
| Editors | Nikhil R. Pal, Michio Sugeno |
| Publisher | Springer Verlag |
| Pages | 310-317 |
| Number of pages | 8 |
| ISBN (Print) | 9783540431503 |
| Publication status | Published - 2002 Jan 1 |
| Event | 5th International Conference on Asian Fuzzy Systems Society, AFSS 2002 - Calcutta, India Duration: 2002 Feb 3 → 2002 Feb 6 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 2275 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 5th International Conference on Asian Fuzzy Systems Society, AFSS 2002 |
|---|---|
| Country | India |
| City | Calcutta |
| Period | 02/2/3 → 02/2/6 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Towards optimal feature and classifier for gene expression classification of cancer. / Ryu, Jungwon; Cho, Sung Bae.
Advances in Soft Computing - AFSS 2002 - 2002 AFSS International Conference on Fuzzy Systems, Proceedings. ed. / Nikhil R. Pal; Michio Sugeno. Springer Verlag, 2002. p. 310-317 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2275).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Towards optimal feature and classifier for gene expression classification of cancer
AU - Ryu, Jungwon
AU - Cho, Sung Bae
PY - 2002/1/1
Y1 - 2002/1/1
N2 - Recently, demand on the tools to efficiently analyze biological genomic information has been on the rise. In this paper, we attempt to explore the optimal features and classifiers through a comparative study with the most promising feature selection methods and machine learning classifiers. In order to predict the cancer class, the gene information from patient’s marrow expressed by DNA microarray, who has either the acute myeloid leukemia or acute lymphoblastic leukemia. Pearson and Spearman’s correlation, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Backpropagation neural network, self-organizing map, structure adaptive self-organizing map, support vector machine, inductive decision tree and k-nearest neighbor have been used for classification. Experimental results indicate that backpropagation neural network with Pearson’s correlation coefficients is the best method, obtaining 97.1% of recognition rate on the test data.
AB - Recently, demand on the tools to efficiently analyze biological genomic information has been on the rise. In this paper, we attempt to explore the optimal features and classifiers through a comparative study with the most promising feature selection methods and machine learning classifiers. In order to predict the cancer class, the gene information from patient’s marrow expressed by DNA microarray, who has either the acute myeloid leukemia or acute lymphoblastic leukemia. Pearson and Spearman’s correlation, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Backpropagation neural network, self-organizing map, structure adaptive self-organizing map, support vector machine, inductive decision tree and k-nearest neighbor have been used for classification. Experimental results indicate that backpropagation neural network with Pearson’s correlation coefficients is the best method, obtaining 97.1% of recognition rate on the test data.
UR - http://www.scopus.com/inward/record.url?scp=23044530914&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=23044530914&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:23044530914
SN - 9783540431503
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 310
EP - 317
BT - Advances in Soft Computing - AFSS 2002 - 2002 AFSS International Conference on Fuzzy Systems, Proceedings
A2 - Pal, Nikhil R.
A2 - Sugeno, Michio
PB - Springer Verlag
ER -