A Survey of Na\"ive Bayes Machine Learning approach in Text Document Classification

TL;DR

This survey reviews Na"ive Bayes machine learning methods for text document classification, highlighting its simplicity, effectiveness on large datasets, and discussing feature selection techniques and evaluation metrics.

Contribution

It provides a comprehensive overview of Na"ive Bayes approaches, feature selection methods, and performance metrics specific to text document classification.

Findings

Na"ive Bayes achieves high accuracy on large datasets.

Feature selection significantly impacts classification performance.

Na"ive Bayes is simple yet effective for text classification tasks.

Abstract

Text Document classification aims in associating one or more predefined categories based on the likelihood suggested by the training set of labeled documents. Many machine learning algorithms play a vital role in training the system with predefined categories among which Na\"ive Bayes has some intriguing facts that it is simple, easy to implement and draws better accuracy in large datasets in spite of the na\"ive dependence. The importance of Na\"ive Bayes Machine learning approach has felt hence the study has been taken up for text document classification and the statistical event models available. This survey the various feature selection methods has been discussed and compared along with the metrics related to text document classification.