A novel bacterial algorithm with randomness control for feature selection in classification

Feature selection (FS) is an essential data-processing technique to reduce the number of features and improve the classification performance, but it is also a challenging problem because of the large search space and complex interactions between features. Bacterial based algorithms (BAs) are effective population based techniques known for their global searching capability. This paper proposes a novel bacterial algorithm based on control mechanisms and modified population updating strategies for feature selection in classification. The proposed new method, abbreviated as BAFS, employs three parameters to control the randomness of the population and reduce the computational complexity by avoiding the redundant searching for the optimal. To make the solutions suitable for feature selection, the strategies of reproduction and elimination are modified according to the classification performance and occurrence of features, respectively. Feature distribution is measured by the probability that features are appeared in the most promising subsets. The proposed bacterial based feature selection algorithm is used for selecting the best feature subsets on datasets with varying dimensionality. Comparison studies on five bacterial based algorithms indicate that the proposed BAFS outperforms other algorithms by achieving higher classification performance.