B. S. Harish

Work place: Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru 570006, India

E-mail: bsharish@sjce.ac.in


Research Interests: Computer systems and computational processes, Computational Learning Theory, Data Mining, Data Structures and Algorithms


B. S. Harish

He obtained his B.E in Electronics and Communication (2002), M.Tech in Networking and Internet Engineering (2004) from Visvesvaraya Technological University, Belagavi, Karnataka, India. He completed his Ph.D. in Computer Science (2011); thesis entitled “Classification of Large Text Data” from University of Mysore. He is presently working as an Associate Professor in the Department of Information Science & Engineering, JSS Science & Technology University, Mysuru. He was invited as a Visiting Researcher to DIBRIS - Department of Informatics, Bio Engineering, Robotics and System Engineering, University of Genova, Italy from May-July 2016. He delivered various technical talks in National and International Conferences. He has invited as a resource person to deliver various technical talks on Data Mining, Image Processing, Pattern Recognition, Soft Computing. He is also serving and served as a reviewer for National, International Conferences and Journals. He has published more than 50 International reputed peer reviewed journals and conferences proceedings. He successfully executed AICTE-RPS project which was sanctioned by AICTE, Government of India. He also served as a secretary, CSI Mysore chapter. He is a Member of IEEE (93068688), Life Member of CSI (09872), Life Member of Institute of Engineers and Life Member of ISTE. His area of interest includes Machine Learning, Text Mining and Computational Intelligence.

Author Articles
A Novel Text Representation Model to Categorize Text Documents using Convolution Neural Network

By M. B. Revanasiddappa B. S. Harish

DOI: https://doi.org/10.5815/ijisa.2019.05.05, Pub. Date: 8 May 2019

This paper presents a novel text representation model called Convolution Term Model (CTM) for effective text categorization. In the process of text categorization, representation plays a very primary role. The proposed CTM is based on Convolution Neural Network (CNN). The main advantage of proposed text representation model is that, it preserves semantic relationship and minimizes the feature extraction burden. In proposed model, initially convolution filter is applied on word embedding matrix. Since, the resultant CTM matrix is higher dimension, feature selection methods are applied to reduce the CTM feature space. Further, selected CTM features are fed into classifier to categorize text document. To discover the effectiveness of the proposed model, extensive experimentations are carried out on four standard benchmark datasets viz., 20-NewsGroups, Reuter-21758, Vehicle Wikipedia and 4 University datasets using five different classifiers. Accuracy is used to assess the performance of classifiers. The proposed model shows impressive results with all classifiers.

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