Random forest ensemble classification based fuzzy logic

Abdelkarim Ben Ayed; Marwa Benhammouda; Mohamed Ben Halima; Adel M. Alimi

doi:10.1117/12.2268564

17 March 2017 Random forest ensemble classification based fuzzy logic

Abdelkarim Ben Ayed, Marwa Benhammouda, Mohamed Ben Halima, Adel M. Alimi

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103412B (2017) https://doi.org/10.1117/12.2268564
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

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.

Citation Download Citation

Abdelkarim Ben Ayed, Marwa Benhammouda, Mohamed Ben Halima, and Adel M. Alimi "Random forest ensemble classification based fuzzy logic", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103412B (17 March 2017); https://doi.org/10.1117/12.2268564

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