Amlan Chakrabarti

Work place: A.K. Choudhury School of Information and Technology Calcutta University Kolkata, India



Research Interests: Algorithm Design, Data Structures and Algorithms, Embedded System, Computer Architecture and Organization, Pattern Recognition, Computational Science and Engineering


Amlan Chakrabarti: He is at present an Associate Professor and HoD at the A.K.Choudhury School of Information Technology, University of Calcutta. He has done his Doctoral research on Quantum Computing and related VLSI design at Indian Statistical Institute, Kolkata, 2004-2008. He was a Post-Doctoral fellow at the School of Engineering, Princeton University, USA during 2011-2012. He is the recipient of BOYSCAST fellowship award in the area of Engineering Science from the Department of Science and Technology Govt. of India in 2011.He has held Visiting Scientist position at the GSI Helmohltz research laboratory Germany and Department of Computer Science and Engineering at the New York State University at Buffalo, U.S.A. during Sept-Oct., 2007. He has published around 50 research papers in referred journals and conferences. He is a Sr. Member of IEEE and life member of Computer Society of India. He has been the reviewer of IEEE Transactions on Computers, IET Computers & Digital Techniques, Elsevier Simulation Modeling Practice and Theory, Springer Journal of Electronic Testing: Theory and Applications. His research interests are: Quantum Computing, VLSI design, Embedded System Design, Video and Image Processing Algorithms and pattern recognition.

Author Articles
A New Evaluation Measure for Feature Subset Selection with Genetic Algorithm

By Saptarsi Goswami Sourav Saha Subhayu Chakravorty Amlan Chakrabarti Basabi Chakraborty

DOI:, Pub. Date: 8 Sep. 2015

Feature selection is one of the most important preprocessing steps for a data mining, pattern recognition or machine learning problem. Finding an optimal subset of features, among all the combinations is a NP-Complete problem. Lot of research has been done in feature selection. However, as the sizes of the datasets are increasing and optimality is a subjective notion, further research is needed to find better techniques. In this paper, a genetic algorithm based feature subset selection method has been proposed with a novel feature evaluation measure as the fitness function. The evaluation measure is different in three primary ways a) It considers the information content of the features apart from relevance with respect to the target b) The redundancy is considered only when it is over a threshold value c) There is lesser penalization towards cardinality of the subset. As the measure accepts value of few parameters, this is available for tuning as per the need of the particular problem domain. Experiments conducted over 21 well known publicly available datasets reveal superior performance. Hypothesis testing for the accuracy improvement is found to be statistically significant.

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Feature Selection: A Practitioner View

By Saptarsi Goswami Amlan Chakrabarti

DOI:, Pub. Date: 8 Oct. 2014

Feature selection is one of the most important preprocessing steps in data mining and knowledge Engineering. In this short review paper, apart from a brief taxonomy of current feature selection methods, we review feature selection methods that are being used in practice. Subsequently we produce a near comprehensive list of problems that have been solved using feature selection across technical and commercial domain. This can serve as a valuable tool to practitioners across industry and academia. We also present empirical results of filter based methods on various datasets. The empirical study covers task of classification, regression, text classification and clustering respectively. We also compare filter based ranking methods using rank correlation.

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Other Articles