NDLI: miDivCon: Framework and method for Multiple Instance Learning

Content Provider	IEEE Xplore Digital Library
Author	Nguyen, C.D. Nguyen, D.T. Cios, K.J. Gardiner, K.J. Costa, A.C.
Copyright Year	2010
Description	Author affiliation: Department of Computer Science, Virginia Commonwealth University, Richmond, VA, U.S.A (Nguyen, C.D.; Nguyen, D.T.; Cios, K.J.) \|\| School of Medicine, University of Colorado, Denver, CO, U.S.A (Gardiner, K.J.; Costa, A.C.)
Abstract	We present a new framework and method for solving Multiple Instance Learning (MIL) problems. As a variation on supervised learning, MIL addresses the problem of classifying a bag of instances. If at least one of the instances in a bag is positive the bag is labeled positive, otherwise it is negative. We use a divide and conquer strategy to identify true positive group of instances in the positive bags and use Bayesian statistics to minimize the false positive instances in the same bags. After testing on benchmark data we also use the method on a challenging task of predicting behavior from molecular profiles data. Comparison results show that our method performs on par or better than other MIL methods.
Starting Page	495
Ending Page	500
File Size	171265
Page Count	6
File Format	PDF
ISBN	9781424481347
e-ISBN	9781424481361
DOI	10.1109/ISDA.2010.5687217
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-29
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines MIL Accuracy Bayesian methods Machine learning Artificial neural networks Bayesian Mice divide and conquer Classification algorithms
Content Type	Text
Resource Type	Article

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