Ahmed Hussein Salman, Waleed Ameen Mahmoud Al-Jawher
{"title":"A Hybrid Multiwavelet Transform with Grey Wolf Optimization Used for an Efficient Classification of Documents","authors":"Ahmed Hussein Salman, Waleed Ameen Mahmoud Al-Jawher","doi":"10.11113/ijic.v13n1-2.418","DOIUrl":null,"url":null,"abstract":"In machine learning, feature selection is crucial to increase performance and shorten the model's learning time. It seeks to discover the pertinent predictors from high-dimensional feature space. However, a tremendous increase in the feature dimension space poses a severe obstacle to feature selection techniques. Study process to address this difficulty, the authors suggest a hybrid feature selection method consisting of the Multiwavelet transform and Gray Wolf optimization. The proposed approach minimizes the overall downsides while cherry picking the benefits of both directions. This notable wavelet transform development employs both wavelet and vector scaling functions. Additionally, multiwavelets have orthogonality, symmetry, compact support, and significant vanishing moments. One of the most advanced areas of study of artificial intelligence is optimization algorithms. Grey Wolf Optimization (GWO) here produced artificial techniques that yielded good performance results and were more responsive to current needs. Keywords — About four key words or phrases in order of importance, separated by commas, used to compile the subject index for the last issue for the year.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"1 1","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Innovative Computing Information and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11113/ijic.v13n1-2.418","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In machine learning, feature selection is crucial to increase performance and shorten the model's learning time. It seeks to discover the pertinent predictors from high-dimensional feature space. However, a tremendous increase in the feature dimension space poses a severe obstacle to feature selection techniques. Study process to address this difficulty, the authors suggest a hybrid feature selection method consisting of the Multiwavelet transform and Gray Wolf optimization. The proposed approach minimizes the overall downsides while cherry picking the benefits of both directions. This notable wavelet transform development employs both wavelet and vector scaling functions. Additionally, multiwavelets have orthogonality, symmetry, compact support, and significant vanishing moments. One of the most advanced areas of study of artificial intelligence is optimization algorithms. Grey Wolf Optimization (GWO) here produced artificial techniques that yielded good performance results and were more responsive to current needs. Keywords — About four key words or phrases in order of importance, separated by commas, used to compile the subject index for the last issue for the year.
期刊介绍:
The primary aim of the International Journal of Innovative Computing, Information and Control (IJICIC) is to publish high-quality papers of new developments and trends, novel techniques and approaches, innovative methodologies and technologies on the theory and applications of intelligent systems, information and control. The IJICIC is a peer-reviewed English language journal and is published bimonthly