Feature selection (FS) has received significant attention since the use of a well-selected subset of features may achieve better classification performance than that of full features in many real-world applications. It can be considered as a multiobjective optimization consisting of two objectives: 1) minimizing the number of selected features and 2) maximizing classification performance. Ant colony optimization (ACO) has shown its effectiveness in FS due to its problem-guided search operator and flexible graph representation. However, there lacks an effective ACO-based approach for multiobjective FS to handle the problematic characteristics originated from the feature interactions and highly discontinuous Pareto fronts. This article presents an Information-theory-based Nondominated Sorting ACO (called INSA) to solve the aforementioned difficulties. First, the probabilistic function in ACO is modified based on the information theory to identify the importance of features; second, a new ACO strategy is designed to construct solutions; and third, a novel pheromone updating strategy is devised to ensure the high diversity of tradeoff solutions. INSA’s performance is compared with four machine-learning-based methods, four representative single-objective evolutionary algorithms, and six state-of-the-art multiobjective ones on 13 benchmark classification datasets, which consist of both low and high-dimensional samples. The empirical results verify that INSA is able to obtain solutions with better classification performance using features whose count is similar to or less than those obtained by its peers.

中文翻译：

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基于信息论的分类多目标特征选择的非支配排序蚁群优化

特征选择（FS）受到了极大的关注，因为在许多实际应用中，使用精心选择的特征子集可以获得比完整特征更好的分类性能。它可以被视为由两个目标组成的多目标优化：1）最小化所选特征的数量和2）最大化分类性能。蚁群优化（ACO）由于其问题引导的搜索算子和灵活的图形表示而在FS中显示出了其有效性。然而，多目标FS缺乏一种有效的基于蚁群算法的方法来处理源自特征交互和高度不连续的帕累托前沿的问题特征。本文提出了一种基于信息论的非支配排序蚁群算法（INSA）来解决上述困难。首先，基于信息论对ACO中的概率函数进行修改，以识别特征的重要性；其次，设计新的ACO策略来构建解决方案；第三，设计了一种新颖的信息素更新策略，以确保权衡解决方案的高度多样性。INSA 的性能在 13 个基准分类数据集（包含低维和高维样本）上与四种基于机器学习的方法、四种代表性的单目标进化算法和六种最先进的多目标算法进行了比较。实证结果验证了 INSA 能够使用与同行获得的特征数量相似或更少的特征来获得具有更好分类性能的解决方案。