Translated Title

A Classification Algorithm CFS-CFW for Reducing Weight Redundancy

Translated Abstract

Naive　Bayes　has　a　strong　independence　assumption,and　feature　weighting　is　the　method　to　solve　this　problem.The　CFW　algorithm　is　a　simple　and　effective　weighting　algorithm,but　its　weight　calculation　formula　incorporates　feature　redundancy,which　affects　the　weight　assigned　to　each　feature　and　reduces　classification　accuracy.In　response　to　the　weight　redundancy　problem　in　the　CFW　algorithm,this　paper　proposes　the　CFS-CFW　algorithm.This　algorithm　uses　the　feature　selection　algorithm　CFS　to　effectively　reduce　weight　redundancy,allowing　each　feature　to　be　assigned　more　appropriate　weights.The　experimental　results　on　13　UCI　datasets　show　that　the　algorithm　has　higher　classification　accuracy.The　accuracy　of　this　algorithm　is　also　higher　on　the　spam　classification　dataset　of　UCI＇s　spam　database.

Translated Keyword

Feature Selection
Feature Weighting
Naive Bayes

Classification

Type

Project Name

Project No.

Access Number

WF:perioarticalfjdn202401003

Language

Chinese

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