Open AccessAn Improved Naive Bayesian Classification Model Based on Attribute Weighting
Author(s) -
Xi Yang,
Mengxuan Tang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1550/2/022017
Subject(s) - weighting , artificial intelligence , computer science , data mining , bayesian probability , naive bayes classifier , pattern recognition (psychology) , machine learning , support vector machine , medicine , radiology
Naive Bayesian model has good classification accuracy and efficiency, which makes it show good performance in many fields, especially in data mining and artificial intelligence. However, the traditional Naive Bayesian classification model ignores the attributes’ independence, resulting in the reduction of classification accuracy. For this reason, an improved model based on attribute fusion and weighting(AWNBC) is proposed, in which data fusion is realized by Spearman coefficient and weighting is realized by average confidence and ReliefF coefficient. The experiment classifies the selected data in UCI database. The result of experiments show that the improved classification model has good classification accuracy and efficiency.