IMPROVING MULTI-LABEL TEXT CLASSIFICATION USING WEIGHTED INFORMATION GAIN AND CO-TRAINED MULTINOMIAL NAÏVE BAYES CLASSIFIER

Abstract

Over recent years, the emergence of electronic text processing systems has generated a vast amount of structured and unstructured data, thus creating a challenging situation for users to rummage through irrelevant information. Therefore, studies are continually looking to improve the classification process to produce more accurate results that would benefit users. This paper looks into the weighted information gain method that re-assigns wrongly classified features with new weights to provide better classification. The method focuses on the weights of the frequency bins, assuming every time a certain word frequency bin is iterated, it provides information on the target word feature. Therefore, the more iteration and re-assigning of weight occur within the bin, the more important the bin becomes, eventually providing better classification. The proposed algorithm was trained and tested using a corpus extracted from dedicated Facebook pages related to diabetes. The weighted information gain feature selection technique is then fed into a co-trained Multinomial Naïve Bayes classification algorithm that captures the labels' dependencies. The algorithm incorporates class value dependencies since the dataset used multi-label data before converting string vectors that allow the sparse distribution between features to be minimised, thus producing more accurate results.  The results of this study show an improvement in classification to 61%.