Random forest ensemble classification based fuzzy logic

被引：0

作者：

Ben Ayed, Abdelkarim ^{[1
]}

Benhammouda, Marwa ^{[1
]}

Ben Halima, Mohamed ^{[1
]}

Alimi, Adel M. ^{[1
]}

机构：

[1] Univ Sfax, ENIS, Res Grp Intelligent Machines, REGIM Lab, BP 1173, Sfax 3038, Tunisia

来源：

NINTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2016) | 2017年 / 10341卷

关键词：

Random Forest; Decision Tree; Fuzzy Logic; Ensemble Classification;

D O I：

10.1117/12.2268564

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：5

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