NDLI: Fast Multiclass SVM Classification Using Decision Tree Based One-Against-All Method

Content Provider	Springer Nature Link
Author	Arun Kumar, M. Gopal, M.
Copyright Year	2010
Abstract	We present an improved version of One-Against-All (OAA) method for multiclass SVM classification based on a decision tree approach. The proposed decision tree based OAA (DT-OAA) is aimed at increasing the classification speed of OAA by using posterior probability estimates of binary SVM outputs. DT-OAA decreases the average number of binary SVM tests required in testing phase to a greater extent when compared to OAA and other multiclass SVM methods. For a balanced multiclass dataset with K classes, under best situation, DT-OAA requires only (K + 1)/2 binary tests on an average as opposed to K binary tests in OAA; however, on imbalanced multiclass datasets we observed DT-OAA to be much faster with proper selection of order in which the binary SVMs are arranged in the decision tree. Computational comparisons on publicly available datasets indicate that the proposed method can achieve almost the same classification accuracy as that of OAA, but is much faster in decision making.
Starting Page	311
Ending Page	323
Page Count	13
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	32
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2010-11-25
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Multiclass classification One-Against-All Decision tree Support Vector Machines (SVMs) Computational Intelligence Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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