Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Product Description
Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Abstract—Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminate capacity for classification.We first revisit two information measures: Kullback-Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their asymptotic
properties relating to type I and type II errors of a Bayesian classifier < final year projects >
Including Packages
Our Specialization
Support Service
Statistical Report
satisfied customers
3,589
Freelance projects
983
sales on Site
11,021
developers
175+
