抽象的

Optimization Algorithms for Feature Selection in Classification: A Survey

S.Ruba Arockia Archana, Dr.M.S.Thanabal

Classification problems have a large number of features in datasets, but not all them are useful for classification. Irrelevant and redundant features reduce the performance. These features may be considered as noisy. In order to solve this problem we perform a feature selection process. It is a preprocessing technique for solving classification problem. Feature Selection aims to choose relevant features to achieve better performance than using all features. Feature selection main objectives are maximizing the classification performance and minimize the features. Inorder to improve the classification accuracy a survey on various Optimization algorithms which uses feature selection for solving classification problem is done.