NDLI: Comparing SVM ensembles for imbalanced datasets

Content Provider	IEEE Xplore Digital Library
Author	Bhatnagar, V. Bhardwaj, M. Mahabal, A.
Copyright Year	2010
Description	Author affiliation: Department of Astronomy, California Institute of Technology, CA 91125, USA (Mahabal, A.) \|\| Department of Computer Science, Delhi University, Delhi, India (Bhatnagar, V.; Bhardwaj, M.)
Abstract	Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are significantly less than those of negative (majority) class leading to severe class imbalance. Constructing high quality classifiers for such imbalanced training data sets is one of the major challenges in machine learning, since traditional classification algorithms tend to get biased towards majority class. In this paper, we compare three ensemble based approaches for handling imbalanced datasets. All the three approaches aim to overcome the under representation of minority class by learning from each of the minority class samples and a subset of majority class samples. The three approaches namely, PARTEN, UMjC and LFM were evaluated on several public datasets. Precision, recall, F-measure, g-mean and ROC space measures were used for comparison. Thread-bare discussion of the results is presented in the paper. Subsequently, we present an astronomy application, where the three methods are compared for prediction of class II, IIn and IIp supernovae.
Starting Page	651
Ending Page	657
File Size	171381
Page Count	7
File Format	PDF
ISBN	9781424481347
e-ISBN	9781424481361
DOI	10.1109/ISDA.2010.5687191
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	Training Support vector machines Supervised Learning Class imbalance Classification Boosting SVM Intelligent systems Kernel Ensembles Astronomy
Content Type	Text
Resource Type	Article

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