Learning Classification Rules via Boolean Compressed Sensing with Application to Workforce Analytics

SpeakerDr. Kush R. Varshney
Organization IBM – Business Analytics and Mathematical Sciences Dept.
LocationEBII 1230
DateOctober 4, 2013 12:50 PM

Abstract:

Motivated by business analytics applications such as identifying employees at risk of voluntary attrition, we propose an interpretable rule-based classification system based on ideas from Boolean compressed sensing. We represent the problem of learning individual conjunctive clauses or individual disjunctive clauses as a Boolean group testing problem, and apply a novel linear programming relaxation to find solutions. We derive results for exact rule recovery which parallel the conditions for exact recovery of sparse signals in the compressed sensing literature. This is an exciting development in rule learning where most prior work focused on heuristic solutions. Furthermore we construct rule sets from these learned clauses using set covering and boosting.  We show competitive classification accuracy using the proposed approach.

Bio:

Kush R. Varshney was born in Syracuse, New York in 1982. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, New York, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science at the Massachusetts Institute of Technology (MIT), Cambridge.

He is a research staff member in the Business Analytics and Mathematical Sciences Department at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY. While at MIT, he was a research assistant with the Stochastic Systems Group in the Laboratory for Information and Decision Systems and a National Science Foundation Graduate Research Fellow. He has been a visiting student at École Centrale, Paris, and an intern at Lawrence Livermore National Laboratory, Sun Microsystems, and Sensis Corporation.

Dr. Varshney is a member of Eta Kappa Nu, Tau Beta Pi, IEEE, and ACM.  He has received best paper awards at the International Conference on Information Fusion and the IEEE International Conference on Service Operations and Logistics, and Informatics; IBM Research Division Awards for business impact of outsourcing analytics and analytics-driven proactive retention in growth markets; and an IBM Outstanding Technical Achievement Award for contributions to salesforce analytics.  He is on the editorial board of Digital Signal Processing.

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