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首页> 外文期刊>Journal of Theoretical and Applied Information Technology >OBKA-FS: AN OPPOSITIONAL-BASED BINARY KIDNEY-INSPIRED SEARCH ALGORITHM FOR FEATURE SELECTION
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OBKA-FS: AN OPPOSITIONAL-BASED BINARY KIDNEY-INSPIRED SEARCH ALGORITHM FOR FEATURE SELECTION

机译：OBKA-FS：基于对数的二进制肾脏启发式搜索算法，用于特征选择

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摘要

Feature selection is a key step when building an automatic classification system. Numerous evolutionary algorithms applied to remove irrelevant features in order to make the classifier perform more accurate. Kidney-inspired search algorithm (KA) is a very modern evolutionary algorithm. The original version of KA performed more effectively compared with other evolutionary algorithms. However, KA was proposed for continuous search spaces. For feature subset selection and many optimization problems such as classification, binary discrete space is required. Moreover, the movement operator of solutions is notably affected by its own best-known solution found up to now, denoted as S_best. This may be inadequate if S_best is located near a local optimum as it will direct the search process to a suboptimal solution. In this study, a three-fold improvement in the existing KA is proposed. First, a binary version of the kidney-inspired algorithm (BKA-FS) for feature subset selection is introduced to improve classification accuracy in multi-class classification problems. Second, the proposed BKA-FS is integrated into an oppositional-based initialization method in order to start with good initial solutions. Thus, this improved algorithm denoted as OBKA-FS. Third, a novel movement strategy based on the calculation of mutual information (MI), which gives OBKA-FS the ability to work in a discrete binary environment has been proposed. For evaluation, an experiment was conducted using ten UCI machine learning benchmark instances. Results show that OBKA-FS outperforms the existing state-of-the-art evolutionary algorithms for feature selection. In particular, OBKA-FS obtained better accuracy with same or fewer features and higher dependency with less redundancy. Thus, the results confirm the high performance of the improved kidney-inspired algorithm in solving optimization problems such as feature selection.
机译：构建自动分类系统时，要素选择是关键步骤。为了使分类器执行起来更准确，应用了许多进化算法来去除不相关的特征。肾启发式搜索算法（KA）是一种非常现代的进化算法。与其他进化算法相比，KA的原始版本性能更高。但是，KA被建议用于连续搜索空间。对于特征子集选择和许多优化问题（例如分类），需要二进制离散空间。此外，解决方案的运动算子特别受其自身迄今发现的最著名解决方案（称为S_best）的影响。如果S_best位于局部最优值附近，这可能是不够的，因为它将将搜索过程引导至次优解决方案。在这项研究中，提出了现有KA的三倍改进。首先，引入了用于特征子集选择的肾脏启发算法（BKA-FS）的二进制版本，以提高多类分类问题中的分类准确性。其次，将所提出的BKA-FS集成到基于对立的初始化方法中，以便从良好的初始解决方案开始。因此，这种改进的算法称为OBKA-FS。第三，提出了一种基于互信息（MI）计算的新颖运动策略，该策略使OBKA-FS能够在离散的二进制环境中工作。为了进行评估，使用了十个UCI机器学习基准实例进行了实验。结果表明，OBKA-FS的性能优于现有的最新特征选择算法。尤其是，OBKA-FS在具有相同或更少功能的情况下获得了更高的准确性，而具有更少冗余的更高依赖性。因此，结果证实了改进的肾脏启发算法在解决诸如特征选择等优化问题方面的高性能。

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