Learning from Imbalanced Data

by Nitesh Chawla

Assistant Professor, Department of Computer Science and Engineering, University of Notre Dame

Date

Tuesday, August 11, 2009

Time

11:00 a.m.

Place

Informatics West, Room 107

Abstract: Recent years brought increased interest in applying data mining techniques to difficult “real-world” problems, many of which are characterized by imbalanced learning data, where at least one class is under-represented relative to others. Examples include (but are not limited to): fraud/intrusion detection, risk management, medical diagnosis/monitoring, bioinformatics, text categorization and personalization of information. The problem of imbalanced data is also often associated with asymmetric costs of misclassifying elements of different classes. In this talk, I will present our work on finding problems in, proposing solutions to, and performing analysis on imbalanced data.

Biography: Nitesh Chawla is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame. He directs the Data Inference Analysis and Learning Lab (DIAL) and co-directs the Interdisciplinary Center of the Network Science and Applications (iCenSA) at Notre Dame. His research is primarily focused on machine learning, data mining, and complex networks. His work has led to applications in various domains including climate data sciences, biology, medicine, finance, security, and social science. He is on the editorial board of IEEE Transactions on Systems, Man and Cybernetics Part B, and has served/serving on the program and organizational committees for a number of top-tier conferences. He has received various awards and honors, including the best dissertation, best papers, outstanding undergraduate teacher, and the NAE New Faculty Fellowship. His current research is supported form NSF, DOD, NWICG, NIJ, and industry sponsors.

Colloquium Provided By:

the School of Informatics