Feature selection for a given model can be transformed into an optimization task. The essential idea behind it is to find the most suitable subset of features according to some criterion. Nature-inspired optimization can mitigate this problem by producing compelling yet straightforward solutions when dealing with complicated fitness functions. Additionally, new mathematical representations, such as quaternions and octonions, are being used to handle higher-dimensional spaces. In this context, we are introducing a meta-heuristic optimization framework in a hypercomplex-based feature selection, where hypercomplex numbers are mapped to real-valued solutions and then transferred onto a boolean hypercube by a sigmoid function. The intended hypercomplex feature selection is tested for several meta-heuristic algorithms and hypercomplex representations, achieving results comparable to some state-of-the-art approaches. The good results achieved by the proposed approach make it a promising tool amongst feature selection research.

中文翻译：

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基于超复杂信息的自然启发特征选择方法

给定模型的特征选择可以转换为优化任务。其背后的基本思想是根据某些准则找到最合适的特征子集。在处理复杂的健身功能时，自然启发式的优化可以通过提供引人注目的却直接的解决方案来缓解此问题。此外，新的数学表示形式，例如四元数和八元数，正被用于处理高维空间。在这种情况下，我们将在基于超复杂度的特征选择中引入元启发式优化框架，其中将超复杂数映射到实值解决方案，然后通过S型函数将其转换为布尔超立方体。针对多种超启发式算法和超复杂表示形式，测试了预期的超复杂特征选择，获得与某些最新方法可比的结果。所提方法取得的良好结果使其成为特征选择研究中有希望的工具。