NDLI: On the selection of decision trees in Random Forests

Content Provider	IEEE Xplore Digital Library
Author	Bernard, S. Heutte, L. Adam, S.
Copyright Year	2009
Description	Author affiliation: LITIS EA 4108, University of Rouen, BP 12 - 76801 Saint-Etienne du Rouvray, France (Bernard, S.; Heutte, L.; Adam, S.)
Abstract	In this paper we present a study on the Random Forest (RF) family of ensemble methods. In a “classical” RF induction process a fixed number of randomized decision trees are inducted to form an ensemble. This kind of algorithm presents two main drawbacks : (i) the number of trees has to be fixed a priori (ii) the interpretability and analysis capacities offered by decision tree classifiers are lost due to the randomization principle. This kind of process in which trees are independently added to the ensemble, offers no guarantee that all those trees will cooperate effectively in the same committee. This statement rises two questions : are there any decision trees in a RF that provide the deterioration of ensemble performance? If so, is it possible to form a more accurate committee via removal of decision trees with poor performance? The answer to these questions is tackled as a classifier selection problem. We thus show that better subsets of decision trees can be obtained even using a sub-optimal classifier selection method. This proves that “classical” RF induction process, for which randomized trees are arbitrary added to the ensemble, is not the best approach to produce accurate RF classifiers. We also show the interest in designing RF by adding trees in a more dependent way than it is traditionally done in “classical” RF induction algorithms.
Starting Page	302
Ending Page	307
File Size	1235073
Page Count	6
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178693
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Radio frequency Classification tree analysis Algorithm design and analysis Bagging Sampling methods Radiofrequency identification Neural networks Training data Boosting
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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