Classification of text documents based on score level fusion approach

• Published in: Pattern recognition letters 2017-07, Vol.94, p.118-126
• Main Authors: Bharath Bhushan, S.N., Danti, Ajit
• Format: Article
• Language: English
• Subjects: Classification; Data mining; Documents; Information processing; Information retrieval; Model accuracy; Neural network; Neural networks; Representations; Text classification; Text representation; Texts; Vector space
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2017.05.003

•An enhanced Sentence–Vector Space Model (S-VSM) model is proposed for the constructive representation of text documents.•An interval valued representation model is proposed for S-VSM, which is computationally less expensive.•Unigram representation technique is proposed to for the effective representation of the text data.•Word level representation method is proposed to capture the semantic information of the document.•For score level fusion, two different classifiers are designed based on the two proposed representation models. 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. Block diagram of the proposed system for Text Classification [Display omitted]