Abstract
This article appoints a novel model of rough set approximations (RSA), namely, rough set approximation models build on containment neighborhoods RSA (CRSA), that generalize the traditional notions of RSA and obtain valuable consequences by minifying the boundary areas. To justify this extension, it is integrated with the binary version of the honey badger optimization (HBO) algorithm as a feature selection (FS) approach. The main target of using this extension is to assess the quality of selected features. To evaluate the performance of BHBO based on CRSA, a set of ten datasets is used. In addition, the results of BHOB are compared with other well-known FS approaches. The results show the superiority of CRSA over the traditional RS approximations. In addition, they illustrate the high ability of BHBO to improve the classification accuracy overall the compared methods in terms of performance metrics.
Highlights
In recent days, the high dimensionality [1] became a big problem [2] in different fields such as human activities recognition [3], silicon-on-insulator FinFETs [4], nonlinear servo systems [5], computer vision [6], processing of IoT data [7], and feature selection
Its average overall, the tested ten datasets are better than other methods with difference 3.780%, 3.7918%, 3.009%, 1.6491%, 3.218%, and 8.0456% when compared with LSHADE, teachinglearning-based optimization (TLBO), salp swarm algorithm (SSA), SGA, selfadaptive differential evolution (SaDE), and bGWO, respectively
We focused on creating a novel model of rough set approximations (RSA), namely, the rough set approximation models depending on containment neighborhoods (CRSA), that generalize the classical notions of (RSA) and derive a number of distinguished results
Summary
The high dimensionality [1] became a big problem [2] in different fields such as human activities recognition [3], silicon-on-insulator FinFETs [4], nonlinear servo systems [5], computer vision [6], processing of IoT data [7], and feature selection. Zou et al [40] introduced the neighborhood RS combined with the fish swarm algorithm for solving the feature selection of some datasets, where the proposed method depended on combining the tolerance rough set (TRS) and firefly algorithm (FA). Such techniques which are used for solving feature selection problems use RS, especially elementary sets according to some classes called equivalence ones where the equivalence can be applied just for complete data, so it may be not suitable for many cases.
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