A selective Bayes Classifier for classifying incomplete data based on gain ratio

Actual data sets are often incomplete because of various kinds of reasons. Although numerous algorithms about classification have been proposed, most of them deal with complete data. So methods of constructing classifiers for incomplete data deserve more attention. By analyzing main methods of proce...

Full description

Saved in:
Bibliographic Details
Published in:Knowledge-based systems 2008-10, Vol.21 (7), p.530-534
Main Authors: Chen, Jingnian, Huang, Houkuan, Tian, Fengzhan, Tian, Shengfeng
Format: Article
Language:English
Subjects:
Algorithms
Automatic classification
Bayesian Classifiers
Data mining
Feature selection
Gain ratio
Incomplete data
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Actual data sets are often incomplete because of various kinds of reasons. Although numerous algorithms about classification have been proposed, most of them deal with complete data. So methods of constructing classifiers for incomplete data deserve more attention. By analyzing main methods of processing incomplete data for classification, this paper presents a selective Bayes Classifier for classifying incomplete data with a simpler formula for computing gain ratio. The proposed algorithm needs no assumption about data sets that are necessary for previous methods of processing incomplete data in classification. Experiments on 12 benchmark incomplete data sets show that this method can greatly improve the accuracy of classification. Furthermore, it can sharply reduce the number of attributes and so can greatly simplify the data sets and classifiers.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2008.03.013