Cancer feature selection and classification using a binary quantum-behaved particle swarm optimization and support vector machine. (English) Zbl 1359.92055
Summary: This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the
number of feature genes selected compared with the other two algorithms.
MSC:
| 92C50 | Medical applications (general) |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 68T05 | Learning and adaptive systems in artificial intelligence |
Keywords:
support vector machine; binary quantum-behaved particle swarm optimization; eave-one-out cross validationReferences:
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