Journal
JOURNAL OF MEDICAL SYSTEMS
Volume 38, Issue 9, Pages -Publisher
SPRINGER
DOI: 10.1007/s10916-014-0097-y
Keywords
Lung cancer; Diagnosis; Genetic algorithm; Feature selection; Machine learning
Funding
- Fundamental Research Funds for the Central Universities [JUDCF12027, JUSRP211A37, JUSRP51323B]
- Fund of the State Key Laboratory of ASIC and System in Fudan University [11KF003]
- PAPD of Jiangsu Higher Education Institutions
- Graduate Student Innovation Program for Universities of Jiangsu Province [CXLX12_0734]
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In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis.
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