An improved hybrid chameleon swarm algorithm for feature selection in medical diagnosis

Feature selection (FS) is generally associated with the process of using a probabilistic method to select optimal feature combinations during pre-processing steps in data mining. This technique can optimize the datasets’ features that need to be considered to heighten the performance of classification on the grounds of the selected optimal feature set. In this paper, a hybridization model is evolved and applied to select the optimal feature subset based on a binary version of the Hybrid Memory Improved Chameleon Swarm Algorithm (CSA) (HMICSA) and the k-Nearest Neighbor (k-NN) classifier. In this FS model, the following are proposed and applied: (1) Four kinds of transfer functions, (2) Amendments to the velocity of the CSA's individuals, (3) Addition of internal memory to the CSA's individuals, and (4) Hybridization of CSA with Ali baba and the Forty Thieves (AFT) algorithm. These actions are aimed to strike an adequate equipoise between global exploration and local exploitation conducts of the search space of the basic CSA. This is to mitigate the problem of early convergence, and to sidestep trapping into a local optima in CSA. The efficacy of the proposed FS algorithm was evaluated on 24 medical diagnosis benchmark datasets collected from different specialized repositories and compared with other k-NN-based FS methods. The all-inclusive outcomes using various evaluation methods disclose the competence of the proposed method in augmenting the classification performance compared to other methods, ensuring its potential in scouting the feature space and designating the most useful features for classification tasks.