Hu Ying, Zhang Yong, Gong Dunwei
IEEE Trans Cybern. 2021 Feb;51(2):874-888. doi: 10.1109/TCYB.2020.3015756. Epub 2021 Jan 15.
Feature selection (FS) is an important data processing technique in the field of machine learning. There have been various FS methods, but all assume that the cost associated with a feature is precise, which restricts their real applications. Focusing on the FS problem with fuzzy cost, a fuzzy multiobjective FS method with particle swarm optimization, called PSOMOFS, is studied in this article. The proposed method develops a fuzzy dominance relationship to compare the goodness of candidate particles and defines a fuzzy crowding distance measure to prune the elitist archive and determine the global leader of particles. Also, a tolerance coefficient is introduced into the proposed method to ensure that the Pareto-optimal solutions obtained satisfy decision makers' preferences. The developed method is used to tackle a series of the UCI datasets and is compared with three fuzzy multiobjective evolutionary methods and three typical multiobjective FS methods. Experimental results show that the proposed method can achieve feature sets with superior performances in approximation, diversity, and feature cost.
特征选择（FS）是机器学习领域中一种重要的数据处理技术。已经有各种各样的FS方法，但所有方法都假定与一个特征相关的成本是精确的，这限制了它们的实际应用。本文针对具有模糊成本的FS问题，研究了一种基于粒子群优化的模糊多目标FS方法，称为PSOMOFS。该方法建立了一种模糊支配关系来比较候选粒子的优劣，并定义了一种模糊拥挤距离度量来修剪精英存档并确定粒子的全局最优解。此外，该方法还引入了一个容忍系数，以确保获得的帕累托最优解满足决策者的偏好。所提出的方法用于处理一系列UCI数据集，并与三种模糊多目标进化方法和三种典型的多目标FS方法进行了比较。实验结果表明，该方法在逼近性、多样性和特征成本方面能够获得性能更优的特征集。
