IIIT Bhubaneswar, India.
Comput Biol Med. 2021 Aug;135:104558. doi: 10.1016/j.compbiomed.2021.104558. Epub 2021 Jun 12.
Dimensionality reduction or Feature Selection (FS) is a multi-target optimization problem with two goals: improving the classification efficiency while simultaneously dropping the characteristics. Harris Hawk Optimization (HHO) is introduced recently to solve different demanding optimization tasks as a metaheuristic tool. The initial HHO is for addressing optimization problems in a continuous environment, but FS is an optimization task in binary space. Therefore, in this article, a Multi-Objective Quadratic Binary HHO (MOQBHHO) technique with K-Nearest Neighbor (KNN) method as wrapper classifier is implemented for extracting the optimal feature subsets. Finally, this study uses the crowding distance (CD) value as a third criterion for picking the best one from the non-dominated solutions. Here, to estimate the performance of the proposed approach, twelve standard medical datasets are considered. The proposed MOQBHHO is compared with MOBHHO-S (using a sigmoid function), multi-objective genetic algorithm (MOGA), multi-objective ant lion optimization (MOALO), and NSGA-II. The experimental findings show that the proposed MOQBHHO finds a set of non-dominated feature subsets effectively in contrast to deep-based FS methods: Auto-encoder and Teacher-Student based FS (TSFS). The presented methodology is found superior in obtaining the best trade-off between two fitness assessment criteria compared to the other existing multi-objective techniques for recognizing relevant features.
降维或特征选择（FS）是一个具有两个目标的多目标优化问题：提高分类效率，同时减少特征。哈里斯鹰优化（HHO）最近被引入作为一种元启发式工具来解决不同的苛刻优化任务。最初的 HHO 是为了解决连续环境中的优化问题，而 FS 是二进制空间中的优化任务。因此，本文提出了一种基于多目标二次二进制 HHO（MOQBHHO）技术和 K-最近邻（KNN）方法作为包装分类器，用于提取最优特征子集。最后，本研究使用拥挤距离（CD）值作为从非支配解中选择最佳解的第三个标准。在这里，为了评估所提出方法的性能，考虑了十二个标准医学数据集。将所提出的 MOQBHHO 与 MOBHHO-S（使用 sigmoid 函数）、多目标遗传算法（MOGA）、多目标蚁狮优化（MOALO）和 NSGA-II 进行了比较。实验结果表明，与基于深度的 FS 方法（自编码器和基于教师-学生的 FS（TSFS）相比，所提出的 MOQBHHO 有效地找到了一组非支配特征子集。与其他现有的多目标技术相比，所提出的方法在获得两个适应度评估标准之间的最佳权衡方面表现出优越性，以识别相关特征。
