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B S Harish

Work place: Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, 570006, India

E-mail: bsharish@jssstuniv.in

Website:

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

Biography

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
Ensemble Feature Selection and Classification of Internet Traffic using XGBoost Classifier

By N Manju B S Harish V Prajwal

DOI: https://doi.org/10.5815/ijcnis.2019.07.06, Pub. Date: 8 Jul. 2019

Identification and classification of internet traffic is most important in network management to ensure Quality of Service (QoS). However, existing machine learning models tend to produce unsatisfactory results when applied with imbalanced datasets involving multiple classes. There are two reasons for this: the models have a bias towards classes which have more samples and they also tend to predict only the majority class data as features of the minority class are often treated as noise and therefore ignored. Thus, there is a high probability of misclassification of the minority class compared with the majority class. Therefore, in this paper, we are proposing an ensemble feature selection based on the tree approach and ensemble classification model using XGboost to enhance the performance of classification. The proposed model achieves better classification accuracy compared to other tree based classifiers.

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