Effective Methods for Improving Naive Bayes Text Classifiers

Authors:
Sang-Bum Kim;Hae-Chang Rim;Dongsuk Yook;HeuiSeok Lim
Affiliations:
-;-;-;-
Venue:
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2002

Citing 7
Cited 16

An example-based mapping method for text categorization and retrieval

ACM Transactions on Information Systems (TOIS)
Pivoted document length normalization

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Machine Learning - Special issue on learning with probabilistic representations
Inductive learning algorithms and representations for text categorization

Proceedings of the seventh international conference on Information and knowledge management
A re-examination of text categorization methods

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval

ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

ECML '98 Proceedings of the 10th European Conference on Machine Learning

Classifying web documents in a hierarchy of categories: a comprehensive study

Journal of Intelligent Information Systems
Text classification: a recent overview

ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
An efficient document classification model using an improved back propagation neural network and singular value decomposition

Expert Systems with Applications: An International Journal
Using the self organizing map for clustering of text documents

Expert Systems with Applications: An International Journal
A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm

Expert Systems with Applications: An International Journal
Automatically computed document dependent weighting factor facility for Naïve Bayes classification

Expert Systems with Applications: An International Journal
What have fruits to do with technology?: the case of Orange, Blackberry and Apple

Proceedings of the International Conference on Web Intelligence, Mining and Semantics
K nearest neighbor reinforced expectation maximization method

Expert Systems with Applications: An International Journal
High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic

Expert Systems with Applications: An International Journal
Techniques for improving the performance of naive bayes for text classification

CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Automatic folder allocation system using Bayesian-support vector machines hybrid classification approach

Applied Intelligence
Secure collaboration in global design and supply chain environment: Problem analysis and literature review

Computers in Industry
A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine

Expert Systems with Applications: An International Journal
An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

Applied Intelligence
CatStream: categorising tweets for user profiling and stream filtering

Proceedings of the 2013 international conference on Intelligent user interfaces
Projected-prototype based classifier for text categorization

Knowledge-Based Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

Though naive Bayes text classifiers are widely used because of its simplicity, the techniques for improving performances of these classifiers have been rarely studied. In this paper, we propose and evaluate some general and effective techniques for improving performance of the naive Bayes text classifier. We suggest document model based parameter estimation and document length normalization to alleviate the problems in the traditional multinomial approach for text classification. In addition, Mutual-Information-weighted naive Bayes text classifier is proposed to increase the effect of highly informative words. Our techniques are evaluated on the Reuters21578 and 20 Newsgroups collections, and significant improvements are obtained over the existing multinomial naive Bayes approach.