| Autor: |
Siddamallappa U, Kumar, Gandhewar, Nisarg |
| Předmět: |
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| Zdroj: |
Multimedia Tools & Applications; May2024, Vol. 83 Issue 18, p54411-54432, 22p |
| Abstrakt: |
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 (Med Biol Eng Comput (Springer) 59(02):333–353 [1]). 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 (J Comput Mater Continua (CMC) 72(1):243–259 [2]). Initial features are selected using a method called "fuzzy c-means clustering with rough set theory" and are subsequently classified using "support vector machines." In addition, the current approach 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 to select the best highlights (IEEE Trans Circuits Syst Video Technol (TCSVT) 28(2):454–467 [3]). 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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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