Text Classification from Positive and Unlabeled Data using Misclassified Data Correction

This paper addresses the problem of dealing with a collection of labeled training documents, especially annotating negative training documents and presents a method of text classification from positive and unlabeled data. We applied an error detection and correction technique to the results of positive and negative documents classified by the Support Vector Machines (SVM). The results using Reuters documents showed that the method was comparable to the current state-of-the-art biasedSVM method as the F-score obtained by our method was 0.627 and biased-SVM was 0.614.