A Fuzzy-Based Fast Feature Selection Using Divide and Conquer Technique in Huge Dimension Dataset

Autor: Mohammad Alahmadi, Wajdi Alghamdi, Harpreet Singh, Prashant Singh Rana, Arihant Tanwar
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Mathematics
Volume 11
Issue 4
Pages: 920
ISSN: 2227-7390
DOI: 10.3390/math11040920
Popis: Feature selection is commonly employed for identifying the top n features that significantly contribute to the desired prediction, for example, to find the top 50 or 100 genes responsible for lung or kidney cancer out of 50,000 genes. Thus, it is a huge time- and resource-consuming practice. In this work, we propose a divide-and-conquer technique with fuzzy backward feature elimination (FBFE) that helps to find the important features quickly and accurately. To show the robustness of the proposed method, it is applied to eight different datasets taken from the NCBI database. We compare the proposed method with seven state-of-the-art feature selection methods and find that the proposed method can obtain fast and better classification accuracy. The proposed method will work for qualitative, quantitative, continuous, and discrete datasets. A web service is developed for researchers and academicians to select top n features.
Databáze: OpenAIRE
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