Document Classification Using Expectation Maximization with Semi Supervised Learning

Bhawna Nigam1, Poorvi Ahirwal2, Sonal Salve3, Swati Vamney4, Department of Information Technology, IET, DAVV 

Abstract

As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is “DYNAMICALLY GENERATION OF NEW CLASS”. The algorithm first trains a classifier using the labeled document and probabilistically classifies the unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side. 

Keywords 

Data mining, semi-supervised learning, supervised learning, expectation maximization, document classification. 

Original Source URL: https://airccse.org/journal/ijsc/papers/2411ijsc04.pdf

https://airccse.org/journal/ijsc/current2011.html

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