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

作     者：Braik, Malik Shehadeh Hammouri, Abdelaziz I. Awadallah, Mohammed A. Al-Betar, Mohammed Azmi Khtatneh, Khalaf 

作者机构：Al Balqa Appl Univ Dept Comp Sci Salt 19117 Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan 

出 版 物：《BIOMEDICAL SIGNAL PROCESSING AND CONTROL》 (生物医学信号处理与控制)

年 卷 期：2023年第85卷第1期

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 10[医学] 

主　　题：Feature selection Chameleon swarm algorithm AFT algorithm Transfer functions Optimization 

摘      要：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.