A Novel Memetic Feature Selection Algorithm
This work addresses feature selection for classification tasks, offering a potentially more efficient solution, but it appears incremental as it builds on existing genetic and filter methods.
The paper tackled the NP-hard feature selection problem by proposing a memetic algorithm that integrates a filter method into a genetic algorithm to improve classification performance and accelerate search for core feature subsets. The method outperformed existing methods on UCI datasets, though no specific accuracy or complexity numbers were provided.
Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for a classification problem. It incorporates a filter method in the genetic algorithm to improve classification performance and accelerates the search in identifying core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the multivariate feature information. Empirical study on commonly data sets of the university of California, Irvine shows that the proposed method outperforms existing methods.