Revisiting Feature Ranking Methods using Information-Centric and
Evolutionary Approaches: Survey

Rashmi       Gandhi; Udayan       Ghose; Hardeo    Kumar   Thakur

doi:10.2174/2210327911666210204142857

Abstract

Abstract: Feature ranking can have a severe impact on the feature selection problem. Feature ranking methods refer to the structure of features that can accept the designed data and have a positive effect on the quality of features. Moreover, accessing useful features helps in reducing cost and improving the performance of a feature ranking algorithm. There are numerous methods for ranking the features that are available in the literature. The developments of the past 20 years in the domain of knowledge research have been explored and presented in terms of relevance and various known concepts of feature ranking problems. The latest developments are mostly based on the evolutionary approaches which broadly include variations in ranking, mutual information, entropy, mutation, parent selection, genetic algorithm, etc. For a variety of algorithms based on differential evolution, it has been observed that although the suitability of the mutation operator is extremely important for feature selection yet other operators can also be considered. Therefore, the special emphasis of various algorithms is observing and reviewing the algorithms and finding new research directions: The general approach is to review a rigorous collection of articles first and then obtain the most accurate and relevant data followed by the narrow down of research questions. Research is based on the research questions. These are reviewed in four phases: designing the review, conducting the review, analyzing, and then writing the review. Threats to validity is also considered with research questions. In this paper, many feature ranking methods have been discussed to find further direction in feature ranking and differential evolution. A literature survey is performed on 93 papers to find out the performance in relevance, redundancy, correlation with differential evolution. Discussion is suitable for cascading the direction of differential evolution in integration with information-theoretic, entropy, and sparse learning. As differential evolution is multiobjective in nature so it can be incorporated with feature ranking problems. The survey is being conducted on many renowned journals and is verified with their research questions. Conclusions of the survey prove to be essential role models for multiple directions of a research entity. In this paper, a comprehensive view on the current-day understanding of the underlying mechanisms describing the impact of algorithms and review current and future research directions for use of evolutionary computations, mutual information, and entropy in the field of feature ranking is complemented by the list of promising research directions. However, there are no strict rules for the pros and cons of alternative algorithms.

Keywords: Feature ranking, evolutionary algorithms, differential evolution, entropy, survey, feature selection.

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DOI https://dx.doi.org/10.2174/2210327911666210204142857	Print ISSN 2210-3279
Publisher Name Bentham Science Publisher	Online ISSN 2210-3287

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