  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.
