The selection feature is an important and fundamental step in the preprocessing of many classification and machine learning problems. The feature selection (FS) method is used to reduce the amount of data used and to create highprobability of classification accuracy (CA) based on fewer features by deleting irrelevant data that often reason confusion for the classifiers. In this work, bat algorithm (BA), which is a new metaheuristic rule, is applied as a wrapper type of FS technique. Six different types of BA (BA-S and BA-V) are proposed, where apiece used a transfer function (TF) to map the solutions from continuous space to the discrete space. The results of the experiment show that the features that use the BA-V methods (that is, the V-shaped transfer function) have proven effective and efficient in selecting subsets of features with high classification accuracy. KeywordsFeature subset selection, Bat algorithm, Transfer function, Metaheuristic algorithms.

中文翻译：

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二进制蝙蝠算法使用不同传递函数的特征选择

选择功能是预处理许多分类和机器学习问题的重要而基本的步骤。特征选择（FS）方法用于减少使用的数据量，并通过删除经常引起分类器混淆的不相关数据，基于较少的特征创建分类精度（CA）的高概率。在这项工作中，蝙蝠算法（BA）是一种新的元启发式规则，被用作FS技术的包装类型。提出了六种不同类型的BA（BA-S和BA-V），其中每个都使用传递函数（TF）来将解决方案从连续空间映射到离散空间。实验结果表明，使用BA-V方法的功能（即 V型传递函数）在选择具有高分类精度的特征子集方面被证明是行之有效的。关键词特征子集选择Bat算法传递函数元启发式算法