Enhanced Classification Accuracy on Naive Bayes Data Mining Models

Weighted naive Bayes modelling for data mining

Naive-Bayes for Sentiment Classification

This report details the findings in building a naive Bayes sentiment classifier for a IMDB movie-review data set using Scala and ScalaNLP. We studied the unigram or bagof-words Bernoulli and Multinomial models and a number of different feature selection techniques, including term frequency, mutual information and Chi-squared. 1. DATA CORPUS The corpus contains of 2000 rated movie reviews, compr...

A Comparison of Event Models for Naive Bayes Text Classification

Recent approaches to text classification have used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-g...

Dynamic Cost-sensitive Naive Bayes Classification for Uncertain Data

The uncertain data as an important aspect of data mining, has received considerable attention, due to its importance in many applications, but little study has been paid to the cost-sensitive classification on uncertain data, so this paper proposes the dynamic costsensitive Naive Bayes classification for mining uncertain data (DCSUNB). Firstly, we apply the probability density to dispose uncert...

Survey of Improving Naive Bayes for Classification

The attribute conditional independence assumption of naive Bayes essentially ignores attribute dependencies and is often violated. On the other hand, although a Bayesian network can represent arbitrary attribute dependencies, learning an optimal Bayesian network classifier from data is intractable. Thus, learning improved naive Bayes has attracted much attention from researchers and presented m...