Now a day there is rapid growth of the World Wide Web. The work of instinctive classification of documents is a main process for organizing the information and knowledge discovery. The classification of e-documents, online bulletin, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing procedures to develop significant information. Therefore, proper classification and information detection from these assets is a fundamental field for study. Text classification is significant study topic in the area of text mining, where the documents are classified with supervised information. In this paper various text representation schemes and learning classifiers such as Naïve Bayes and Decision Tree algorithms are described with illustration for predefined classes. This present approaches are compared and distinguished based on quality assurance parameters.

Keywords : Classifiers, Machine learning, Supervised learning, Text classification.