Paper Info
Title
On Why Discretization Works for Naive-Bayes Classifiers
Abstract
We investigate why discretization can be effective in naive-Bayes learning. We prove a theorem that identifies particular conditions under which discretization will result in naive-Bayes classifiers delivering the same probability estimates as would be obtained if the correct probability density functions were employed. We discuss the factors that might affect naive-Bayes classification error under discretization. We suggest that the use of different discretization techniques can affect the classification bias and variance of the generated classifiers. We argue that by properly managing discretization bias and variance, we can effectively reduce naive-Bayes classification error.
Year
DOI
Venue
2003
10.1007/978-3-540-24581-0_37
AI 2003: ADVANCES IN ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
naive bayes,probability density function,naive bayes classifier
Discrete mathematics,Discretization,Discretization error,Pattern recognition,Naive Bayes classifier,Error tolerance,Computer science,Algorithm,Artificial intelligence,Probability density function,Decision boundary,Discretization of continuous features
Conference
Volume
ISSN
Citations 
2903
0302-9743
40
PageRank 
References 
Authors
2.03
31
2
Name
Order
Citations
PageRank
Ying Yang120610.51
Geoffrey I. Webb23130234.10