Semi-supervised text classification using enhanced KNN algorithm

Abstract

Due to the growth of information which has a great value, classifying the available information becomes inevitable so that navigation could be made easy. Many techniques of supervised learning and unsupervised learning do exist in the literature for data classification. Semi-supervised learning is halfway between the supervised and unsupervised learning. In addition to unlabeled data, the algorithm is provided with some supervision information but not necessarily for all example data. The paper explores the semi-supervised text classification which is applied to different types of vectors that are generated from the text documents. Enhancements in KNN algorithm are made to increase the accuracy performance of the classifier in the process of semi-supervised text classification, and results obtained are encouraging.