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Multi-objective Optimization for Incremental Decision Tree Learning
Hang Yang; Simon Fong; Yain-Whar Si
Conference Name14th International Conference, DaWaK 2012
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7448 LNCS
Conference DateSeptember 3-6, 2012
Conference PlaceVienna, Austria

Decision tree learning can be roughly classified into two categories: static and incremental inductions. Static tree induction applies greedy search in splitting test for obtaining a global optimal model. Incremental tree induction constructs a decision model by analyzing data in short segments; during each segment a local optimal tree structure is formed. Very Fast Decision Tree [4] is a typical incremental tree induction based on the principle of Hoeffding bound for node-splitting test. But it does not work well under noisy data. In this paper, we propose a new incremental tree induction model called incrementally Optimized Very Fast Decision Tree (iOVFDT), which uses a multi-objective incremental optimization method. iOVFDT also integrates four classifiers at the leaf levels. The proposed incremental tree induction model is tested with a large volume of data streams contaminated with noise. Under such noisy data, we investigate how iOVFDT that represents incremental induction method working with local optimums compares to C4.5 which loads the whole dataset for building a globally optimal decision tree. Our experiment results show that iOVFDT is able to achieve similar though slightly lower accuracy, but the decision tree size and induction time are much smaller than that of C4.5. 

KeywordClassification Decision Tree Incremental Optimization Stream Mining
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Document TypeConference paper
AffiliationDepartment of Science and Technology, University of Macau, Av. Padre Tomás Pereira Taipa, Macau, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Hang Yang,Simon Fong,Yain-Whar Si. Multi-objective Optimization for Incremental Decision Tree Learning[C],2012:217-228.
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