Improved Harmony Search with Optimal Deep Learning Enabled Classification Model

作     者：Mahmoud Ragab Adel A.Bahaddad 

作者机构：Information Technology DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision MedicinesKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Information Systems DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第10期

页      面：1783-1797页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：This work was funded by the Deanship of Scientific Research(DSR) King Abdulaziz University Jeddah under Grant No.(D-914-611-1443) 

主　　题：Data classification feature selection global optimization deep learning metaheuristics 

摘      要：Due to drastic increase in the generation of data,it is tedious to examine and derive high level knowledge from the *** rising trends of high dimension data gathering and problem representation necessitates feature selection process in several machine learning *** feature selection procedure establishes a generally encountered issue of global combinatorial *** FS process can lessen the number of features by the removal of unwanted and repetitive *** this aspect,this article introduces an improved harmony search based global optimization for feature selection with optimal deep learning(IHSFS-ODL)enabled classification *** proposed IHSFS-ODL technique intends to reduce the curse of dimensionality and enhance classification *** addition,the IHSFSODL technique derives an IHSFS technique by the use of local search method with traditional harmony search algorithm(HSA)for global ***,ODL based classifier including quantum behaved particle swarm optimization(QPSO)with gated recurrent unit(GRU)is applied for data classification *** utilization of HSA for the choice of features and QPSO algorithm for hyper parameter tuning processes helps to accomplish maximum classification *** order to demonstrate the enhanced outcomes of the IHSFS-ODL technique,a series of simulations were carried out and the results reported the betterment over its recent state of art approaches.