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AIC Seminar Series

Could Random Inductive Models be Better than Well-Thought Models?

Wei FanIBM T.J. Watson Research Center[Home Page]

Notice:  hosted by Guizhen Yang

Date:  2006-06-22 at 16:00

Location:  EJ228  (Directions)

   Abstract

Inductive learning is to construct an accurate model from labeled training examples to match the true model that generates these training instances. One practical example is to estimate someone’s likelihood to default on a mortgage. The major difficulty for inductive learning is that the true model is never known for many real world problems. Any assumption about the exact form of the true model could be wrong. In reality, this is even difficult to verify since labeled examples are none-exhaustive for most applications. The main stream research of machine learning in the past 20 years has been focusing on rather sophisticated and well-thought approaches to approximating the true models in classification, regression and probability estimation problems. Examples of well-known algorithms belonging to this family include Boosting, Bagging, SVM, Mixture models, Logistic Regression, and GUIDE, among others. In this talk, we will introduce Randomized Decision Trees or RDT that can be used efficiently and accurately for classification, regression and probability estimation problems. The training procedure of RDT incorporates some surprisingly simple and unconventional random factors. However, its accuracy in all the three major problems is either higher or significantly higher than many well-known sophisticated approaches. In summary, this talk offers the following insights: 1. Introduction of Randomized Decision Trees and its application in classification, regression, and probability estimation. Several applications of RDT, such as equity trading fraud detection, customer default payment prediction, information retrieval, storage component latency modeling, and ground ozone level estimation will be included. 2. A fresh and unconventional look at accurate machine learning and data mining without making strong assumptions.

   Bio for Wei Fan

Dr. Wei Fan received his PhD in Computer Science from Columbia University in 2000. He published more than 50 papers in top data mining, machine learning and database conferences/journals, such as KDD, SDM, ICDM, SIGMOD, VLDB, ICDE, AAAI, etc. Dr. Fan has served as VC and PC of several prestigious conferences in the area including KDD’06/05, ICDM’06/05/04/03, SDM’06/05/04, CIKM’06, ECML/PKDD’06, and ICDE’04. His main research interests and experiences are in risk analysis, high performance computing, extremely skewed distribution, cost-sensitive learning, data streams, ensemble methods, and commercial data mining systems. He is particularly interested in simple, unconventional, but effective methods to solve difficult problems. Much of his works on each of these areas are well known and well-cited. His thesis work on intrusion detection has been licensed by a start-up company since 2001. His co-authored paper in KDD’97 on distributed learning system "JAM" won the runner-up best research paper award. He won IBM invention achievement awards in 2002, 2003 and 2004.

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