NDLI: Classifier Ensembles with the Extended Space Forest

Content Provider	IEEE Xplore Digital Library
Author	Amasyali, M.F. Ersoy, O.K.
Copyright Year	1989
Abstract	The extended space forest is a new method for decision tree construction in which training is done with input vectors including all the original features and their random combinations. The combinations are generated with a difference operator applied to random pairs of original features. The experimental results show that extended space versions of ensemble algorithms have better performance than the original ensemble algorithms. To investigate the success dynamics of the extended space forest, the individual accuracy and diversity creation powers of ensemble algorithms are compared. The Extended Space Forest creates more diversity when it uses all the input features than Bagging and Rotation Forest. It also results in more individual accuracy when it uses random selection of the features than Random Subspace and Random Forest methods. It needs more training time because of using more features than the original algorithms. But its testing time is lower than the others because it generates less complex base learners.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	549
Ending Page	562
Page Count	14
File Size	3094634
File Format	PDF
ISSN	10414347
Volume Number	26
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Bagging Heuristic algorithms Classification algorithms Testing Decision trees Accuracy supervised learning Classifier ensembles committees of learners consensus theory ensemble algorithms mixtures of experts multiple classifier systems extended spaces bagging random forest random subspace rotation forest decision trees
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

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