关键词: 特征选择, 生物数据, 人工蜂群算法, 化学反应优化算法, 精英保留策略

Abstract: The high-dimensional data set contains thousands of features that can be used for data analyzing and forecasting. However, these data sets have many unrelated or redundant features that affect the accuracy of data analyzing and forecasting. Existing classification techniques are difficult to accurately identify the best subset of features. Aiming at this problem, this paper proposes a feature selection method AB-CRO based on wrapper mode, which combines the advantages of Artificial Bee Colony algorithm（ABC） and improved Chemical Reaction algorithm（CRO） to select features. In view of the phenomenon that the superior individuals in the iterative process may be consumed during the chemical reaction process, an elite strategy is appropriately added to maintain the superiority of the population. It presents a proposed method comparing the benchmark algorithms ABC, CRO, and biometric selection methods based on GA, PSO, and ISFLA on public data sets. The experimental results show that the proposed algorithm improves in the recognition and classification accuracy of related subsets.

Key words: feature selection, biomedical dataset, artificial bee colony algorithm, chemical reaction optimization algorithm, elite retention strategy