蚁群优化算法
计算机科学
人工蜂群算法
特征选择
水准点(测量)
群体智能
蜜蜂算法
人工智能
选择(遗传算法)
特征(语言学)
元启发式
蚁群
混合算法(约束满足)
群体行为
启发式
模式识别(心理学)
数学优化
机器学习
粒子群优化
数学
大地测量学
地理
约束逻辑程序设计
语言学
约束满足
概率逻辑
哲学
作者
P. Shunmugapriya,S. Kanmani
标识
DOI:10.1016/j.swevo.2017.04.002
摘要
Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO) are famous meta-heuristic search algorithms used in solving numerous combinatorial optimization problems. Feature Selection (FS) helps to speed up the process of classification by extracting the relevant and useful information from the dataset. FS is seen as an optimization problem because selecting the appropriate feature subset is very important. This paper proposes a novel Swarm based hybrid algorithm AC-ABC Hybrid, which combines the characteristics of Ant Colony and Artificial Bee Colony (ABC) algorithms to optimize feature selection. By hybridizing, we try to eliminate stagnation behavior of the ants and time consuming global search for initial solutions by the employed bees. In the proposed algorithm, Ants use exploitation by the Bees to determine the best Ant and best feature subset; Bees adapt the feature subsets generated by the Ants as their food sources. Thirteen UCI (University of California, Irvine) benchmark datasets have been used for the evaluation of the proposed algorithm. Experimental results show the promising behavior of the proposed method in increasing the classification accuracies and optimal selection of features.
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