A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees
This work addresses the need for more accurate classification methods in data mining and AI, though it appears incremental as it builds on existing random forest and fuzzy theory.
The authors tackled the problem of improving classification accuracy by proposing an intuitionistic fuzzy random forest (IFRF), which combines random forest with intuitionistic fuzzy decision trees, and demonstrated competitive or superior performance compared to other state-of-the-art fuzzy and ensemble algorithms.
Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex conditions. Although it has been widely adopted, its combination with diverse fuzzy theory is still worth exploring. In this paper, we propose the intuitionistic fuzzy random forest (IFRF), a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT). Such trees in forest use intuitionistic fuzzy information gain to select features and consider hesitation in information transmission. The proposed method enjoys the power of the randomness from bootstrapped sampling and feature selection, the flexibility of fuzzy logic and fuzzy sets, and the robustness of multiple classifier systems. Extensive experiments demonstrate that the IFRF has competitative and superior performance compared to other state-of-the-art fuzzy and ensemble algorithms. IFDT is more suitable for ensemble learning with outstanding classification accuracy. This study is the first to propose a random forest ensemble based on the intuitionistic fuzzy theory.