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A hybrid SVM based decision tree . Presenter: Tsai Tzung Ruei Authors: M. ArunKumar n, M.Gopal. 國立雲林科技大學 National Yunlin University of Science and Technology. PR.2010. Outline. Motivation Objective Methodology Experiments Conclusion Comments. Motivation. - PowerPoint PPT Presentation
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
A hybrid SVM based decision tree
Presenter: Tsai Tzung Ruei Authors: M. ArunKumar n, M.Gopal
PR.2010
國立雲林科技大學National Yunlin University of Science and Technology
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Outline
Motivation Objective Methodology Experiments Conclusion Comments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Motivation
SVMs are considerably slower in testing phase than other techniques. This is because the computational complexity of SVM’s decision function scales with respect to the number of support vectors. Hence if the number of support vectors is very large, SVMs will take more time to classify a new datapoint.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Objective
To proposed a hybrid SVM based decision tree to speedup SVMs in its testing phase for binary classification tasks.
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The objective To predict whether a household has an income greater than $50 k.
The outcomes(1) DTs are much faster than SVMs in classifying new instances.(2) SVMs perform better then DTs in terms of classification accuracy.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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SVMDT
SVMDT
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
SVMDT algorithm
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Train_data: set of training datapoints Train_target: corresponding target for Train_data New_target: targets to be used for DT training
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
SVMDT algorithm
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Class3
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
Adult datasets
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
Checkerboard dataset
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The result:The classification accuracy of SVMDT was same as that of SVM
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
SVMDT results on other binary datasets
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
SVMDT comparison with FVS
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Conclusion
MAJOR CINTRIBUTION On all the datasets, SVMDT has shown impressive results with
significant speedup when compared to SVM, without any compromise in classification accuracy.
FUTURE WORK To realize the potential of SVMDT in multiclass classification.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Comments
Advantage Create a novel way of decreasing testing time of SVMs and it does not
contradict with the existing approaches.
Drawback ……
Application classification
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