Department of studies in Computer Science, Davangere University, Davangere, India.
Review Article
World Journal of Advanced Research and Reviews, 2023, 19(03), 1170-1176
Article DOI: 10.30574/wjarr.2023.19.3.2005
Received on 16 August 2023; revised on 24 September 2023; accepted on 27 September 2023
Feature selection is one of the major components of the data processing flow that correctly selects real-time entities for categorization. It is utilized in numerous research fields, including the machine learning approach. Feature selection and classification are beneficial to the processing of biomedical data in high-importance, high-dimensional datasets. Due to numerous obstacles in research, the current implementations are inadequate for predicting classification accuracy. To handle the classification challenge, we require dedicated neural network and classification model approaches. Initial features are selected using a method called "fuzzy c-means clustering with rough set theory" and are subsequently classified using "support vector machines." The learning machines' accuracy is seriously harmed by irrelevant and redundant features. The curse of dimensionality makes it difficult to locate data clusters in high-dimensional space. Data in the irrelevant dimensions may cause a lot of noise when the dimensionality grows. In addition, the current approach has a significant flaw: it is extremely time-consuming. The proposed solution used efficient feature subset selection in high-dimensional data to fix these problems. To begin with, our recommended method used Enhanced Social Spider Optimization (ESSO) computation, in which the standard Social Spider Optimization is improved with the aid of optimal radial based calculation to select the best highlights. When categorizing data, the Optimal Radial Basis Function Neural Network (ORBFNN) is used. Methods for calculating Artificial Bee Colony (ABC) are used to streamline RBFNN's sufficiency in characterizing smaller scale show information.
ESSO; RBFNN; ORBFNN; ABC
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Kumar Siddamallappa U. Feature selection for image classification using ESSO. World Journal of Advanced Research and Reviews, 2023, 19(3), 1170-1176. Article DOI: https://doi.org/10.30574/wjarr.2023.19.3.2005