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Proceedings Paper

Random forest ensemble classification based fuzzy logic
Author(s): Abdelkarim Ben Ayed; Marwa Benhammouda; Mohamed Ben Halima; Adel M. Alimi
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Paper Abstract

In this paper, we treat the supervised data classification, while using the fuzzy random forests that combine the hardiness of the decision trees, the power of the random selection that increases the diversity of the trees in the forest as well as the flexibility of the fuzzy logic for noise. We will be interested in the construction of a forest of fuzzy decision trees. Our system is validated on nine standard classification benchmarks from UCI repository and have the specificity to control some data, to reduce the rate of mistakes and to put in evidence more of hardiness and more of interoperability.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103412B (17 March 2017); doi: 10.1117/12.2268564
Show Author Affiliations
Abdelkarim Ben Ayed, Research Groups in Intelligent Machines (Tunisia)
Marwa Benhammouda, Research Groups in Intelligent Machines (Tunisia)
Mohamed Ben Halima, Research Groups in Intelligent Machines (Tunisia)
Adel M. Alimi, Research Groups in Intelligent Machines (Tunisia)


Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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