Classification of text documents based on score level fusion approach

摘要

Text document classification is a well known theme in the field of the information retrieval and text mining. Selection of most desired features in the text document plays a vital role in classification problem. This research article addresses the problem of text classification by considering Sentence-Vector Space Model (S-VSM) and Unigram representation models for the text document. An enhanced S-VSM model will be considered for the constructive representation of text documents. A neural network based representation for text documents is proposed for effective capturing of semantic information of the text data. Two different classifiers are designed based on the two different representation models of the text documents. Score level fusion is applied on two proposed models to find out the overall accuracy of the proposed model. Key contributions of the paper are an enhanced S-VSM model, an interval valued representation model for the proposed S-VSM approach. A word level representation model for semantic information preserving of the text document and score level fusion approach. (C) 2017 Elsevier B.V. All rights reserved.