TY - GEN
T1 - A constraint and rule in an enhancement of binary particle swarm optimization to select informative genes for cancer classification
AU - Mohamad, Mohd Saberi
AU - Omatu, Sigeru
AU - Deris, Safaai
AU - Yoshioka, Michifumi
PY - 2013
Y1 - 2013
N2 - Gene expression data have been analyzing by many researchers by using a range of computational intelligence methods. From the gene expression data, selecting a small subset of informative genes can do cancer classification. Nevertheless, many of the computational methods face difficulties in selecting small subset since the small number of samples needs to be compared to the huge number of genes (high-dimension), irrelevant genes and noisy genes. Hence, to choose the small subset of informative genes that is significant for the cancer classification, an enhanced binary particle swarm optimization is proposed. Here, the constraint of the elements of particle velocity vectors is introduced and a rule for updating particle's position is proposed. Experiments were performed on five different gene expression data. As a result, in terms of classification accuracy and the number of selected genes, the performance of the introduced method is superior compared to the conventional version of binary particle swarm optimization (BPSO). The other significant finding is lower running times compared to BPSO for this proposed method.
AB - Gene expression data have been analyzing by many researchers by using a range of computational intelligence methods. From the gene expression data, selecting a small subset of informative genes can do cancer classification. Nevertheless, many of the computational methods face difficulties in selecting small subset since the small number of samples needs to be compared to the huge number of genes (high-dimension), irrelevant genes and noisy genes. Hence, to choose the small subset of informative genes that is significant for the cancer classification, an enhanced binary particle swarm optimization is proposed. Here, the constraint of the elements of particle velocity vectors is introduced and a rule for updating particle's position is proposed. Experiments were performed on five different gene expression data. As a result, in terms of classification accuracy and the number of selected genes, the performance of the introduced method is superior compared to the conventional version of binary particle swarm optimization (BPSO). The other significant finding is lower running times compared to BPSO for this proposed method.
KW - Binary particle swarm optimization
KW - Gene expression data
KW - Gene selection
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=84892860398&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84892860398&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40319-4_15
DO - 10.1007/978-3-642-40319-4_15
M3 - Conference contribution
AN - SCOPUS:84892860398
SN - 9783642403187
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 168
EP - 178
BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops
T2 - 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Y2 - 14 April 2013 through 17 April 2013
ER -