title: An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method.
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.
authors.affiliation: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Department of Electronic Engineering, Jiangnan University, Wuxi, 214122, China.
authors.forename: Chunhong
authors.initials: C
authors.lastname: Lu
authors.forename: Zhaomin
authors.initials: Z
authors.lastname: Zhu
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Gu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D055641:Mathematical Concepts; D016571:Neural Networks, Computer; D060388:Support Vector Machine