NDLI: Hellinger distance decision trees are robust and skew-insensitive

Content Provider	Springer Nature Link
Author	Cieslak, David A. Hoens, T. Ryan Chawla, Nitesh V. Kegelmeyer, W. Philip
Copyright Year	2011
Abstract	Learning from imbalanced data is an important and common problem. Decision trees, supplemented with sampling techniques, have proven to be an effective way to address the imbalanced data problem. Despite their effectiveness, however, sampling methods add complexity and the need for parameter selection. To bypass these difficulties we propose a new decision tree technique called Hellinger Distance Decision Trees (HDDT) which uses Hellinger distance as the splitting criterion. We analytically and empirically demonstrate the strong skew insensitivity of Hellinger distance and its advantages over popular alternatives such as entropy (gain ratio). We apply a comprehensive empirical evaluation framework testing against commonly used sampling and ensemble methods, considering performance across 58 varied datasets. We demonstrate the superiority (using robust tests of statistical significance) of HDDT on imbalanced data, as well as its competitive performance on balanced datasets. We thereby arrive at the particularly practical conclusion that for imbalanced data it is sufficient to use Hellinger trees with bagging (BG) without any sampling methods. We provide all the datasets and software for this paper online ( http://www.nd.edu/~dial/hddt ).
Starting Page	136
Ending Page	158
Page Count	23
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	24
Issue Number	1
e-ISSN	1573756X
Language	English
Publisher	Springer US
Publisher Date	2011-06-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Imbalanced data Decision tree Hellinger distance Artificial Intelligence (incl. Robotics) Statistics Data Mining and Knowledge Discovery Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Computing Methodologies Information Storage and Retrieval
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Computer Science Applications

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