1. An Intelligent System for Lung Cancer Diagnosis Using a New Genetic Algorithm Based Feature Selection Method.
- Author
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Lu, Chunhong, Zhu, Zhaomin, and Gu, Xiaofeng
- Subjects
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LUNG tumors , *ACADEMIC medical centers , *ALGORITHMS , *RESEARCH funding , *GENOMICS , *DIAGNOSIS - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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