Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10916-014-0097-y | Journal of Medical Systems |
Keywords | Field | DocType |
lung cancer,diagnosis,genetic algorithm,feature selection,machine learning | k-nearest neighbors algorithm,Data mining,Feature selection,Pattern recognition,Support vector machine,Cardinality,Correlation,Mutual information,Artificial intelligence,Artificial neural network,Medicine,Genetic algorithm | Journal |
Volume | Issue | ISSN |
38 | 9 | 1573-689X |
Citations | PageRank | References |
76 | 0.68 | 104 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunhong Lu | 1 | 77 | 1.06 |
| Zhaomin Zhu | 2 | 81 | 2.50 |
| Xiaofeng Gu | 3 | 113 | 14.72 |