TY - GEN
T1 - Selecting genes from gene expression data by using an enhancement of binary particle swarm optimization for cancer classification
AU - Mohamad, Mohd Saberi
AU - Omatu, Sigeru
AU - Yoshioka, Michifumi
AU - Deris, Safaai
PY - 2010
Y1 - 2010
N2 - In order to select a small subset of informative genes from gene expression data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an enhancement of binary particle swarm optimization to select a small subset of informative genes that is relevant for classifying cancer samples more accurately. In this proposed method, three approaches have been introduced to increase the probability of bits in particle's positions to be zero. By performing experiments on three different gene expression data sets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
AB - In order to select a small subset of informative genes from gene expression data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an enhancement of binary particle swarm optimization to select a small subset of informative genes that is relevant for classifying cancer samples more accurately. In this proposed method, three approaches have been introduced to increase the probability of bits in particle's positions to be zero. By performing experiments on three different gene expression data sets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
KW - Binary particle swarm optimization
KW - Cancer classification
KW - Gene expression data
KW - Gene selection
UR - http://www.scopus.com/inward/record.url?scp=77956296797&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956296797&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77956296797
SN - 9789896740221
SN - 9789896740214
T3 - ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
SP - 82
EP - 89
BT - ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
T2 - 2nd International Conference on Agents and Artificial Intelligence, ICAART 2010
Y2 - 22 January 2010 through 24 January 2010
ER -