Feature selection in classification using self-adaptive owl search optimization algorithm with elitism and mutation strategies
Feature selection in classification using self-adaptive owl search optimization algorithm with elitism and mutation strategies
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DOI：
10.3233/jifs-200258
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. K. Mandal;Rikta Sen;B. Chakraborty
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文献类型：
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作者：
A. K. Mandal;Rikta Sen;B. Chakraborty
特征选择的根本目的是通过去除不相关和冗余的特征来降低数据的维度。由于从所有可能的子集中找到最佳特征子集的计算代价很高，特别是对于高维数据集，元启发式算法经常被用作解决该任务的一种有前途的方法。提出了一种基于包装器的特征选择算法，该算法是最近提出的一种新的元启发式Owl搜索优化算法。结合了几种策略，目的是加强BOSA(OSA的二进制版本)在搜索全球最佳解决方案方面的能力。在搜索过程中，BOSA的元参数被动态初始化，然后使用自适应机制进行调整。此外，精英主义和变异操作与BOSA相结合，更好地控制了开采和勘探。本文将这种改进的BOSA算法命名为改进的二元猫头鹰搜索算法(MBOSA)。使用决策树(DT)分类器作为基于包装器的适应度函数，并利用支持向量机(SVM)分类器对所选特征子集的最终分类性能进行评估。在UCI的20个知名基准数据集上进行了仿真实验，并根据分类精度、选择的特征数量和执行时间报告了结果。此外，本文还将BOSA算法与三种常用的元启发式算法--二进制BAT算法(BBA)、二进制粒子群算法(BPSO)和二进制遗传算法(BGA)进行了比较。仿真结果表明，该方法在保持相当的分类精度的情况下，显著减少了特征的个数，性能优于同类方法。
The fundamental aim of feature selection is to reduce the dimensionality of data by removing irrelevant and redundant features. As finding out the best subset of features from all possible subsets is computationally expensive, especially for high dimensional data sets, meta-heuristic algorithms are often used as a promising method for addressing the task. In this paper, a variant of recent meta-heuristic approach Owl Search Optimization algorithm (OSA) has been proposed for solving the feature selection problem within a wrapper-based framework. Several strategies are incorporated with an aim to strengthen BOSA (binary version of OSA) in searching the global best solution. The meta-parameter of BOSA is initialized dynamically and then adjusted using a self-adaptive mechanism during the search process. Besides, elitism and mutation operations are combined with BOSA to control the exploitation and exploration better. This improved BOSA is named in this paper as Modified Binary Owl Search Algorithm (MBOSA). Decision Tree (DT) classifier is used for wrapper based fitness function, and the final classification performance of the selected feature subset is evaluated by Support Vector Machine (SVM) classifier. Simulation experiments are conducted on twenty well-known benchmark datasets from UCI for the evaluation of the proposed algorithm, and the results are reported based on classification accuracy, the number of selected features, and execution time. In addition, BOSA along with three common meta-heuristic algorithms Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Binary Genetic Algorithm (BGA) are used for comparison. Simulation results show that the proposed approach outperforms similar methods by reducing the number of features significantly while maintaining a comparable level of classification accuracy.