# An opposition-based social spider optimization for feature selection

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## Abstract

In machine learning and data mining, feature selection (FS) is one of the most important tasks required to select the most relevant instances from a dataset. In other words, FS is used to reduce the amount of information, creating a subset that represents the entire pool of data. The accuracy of the FS is reflected in a good classification of the information. This article presents an improved version of the social spider optimization (SSO) algorithm. The SSO tends to fail in local optima during the iterative process and is not possible to avoid this situation in the standard form. The proposed version avoids selecting the irrelevant features that demerit the performance of the FS. To achieve this goal, the opposition-based learning is used, in which there is a rule used to increase the exploration of the search space and the prominent zones in a determined neighborhood. The proposed algorithm is called opposition-based social spider optimization (OBSSO), and it has been tested over different mathematical problems. Moreover, the OBSSO, also, has been tested and compared with similar approaches using different datasets with specific information selected from UCI repository. The experimental results provide the evidence of the capabilities of the OBSSO for solving complex optimization problems.

## Keywords

Meta-heuristic (MH) Social spider optimization (SSO) Opposition-based learning (OBL) Feature selection (FS)## Notes

### Acknowledgements

This work is supported by the Science and Technology Program of Shenzhen of China under Grant Nos. JCYJ20170818160208570 and JCYJ20170307160458368. This study was carried out without any funding sources.

### Compliance with ethical standards

### Conflict of interest

All authors declare that they have no conflict interest.

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