Enhanced Fuzzy Random Forest by Using Doubly Randomness and Copying From Dynamic Dictionary Attributes

Abstract

While fuzzy random forest (FRF) as a fuzzy implementation of random forest has earned its strong ambiguity/uncertainty handling capability on a rich variety of considerably low dimensional datasets, this article revisits FRF and attempts to enhance its generalization capability and computational speed on high dimensional datasets. For the first issue, in addition to the original use of randomness in FRF, a doubly randomness is newly introduced into the generation of both the candidate attributes and the best splitting attributes in FRF. For the computational speed issue, while the proposed new fuzzy information gain (NFG) measure does not apply to all candidate attributes, the remaining NFG values can be quickly retrieved by copying from the dynamically generated dictionary. As a result, a new method called enhanced fuzzy random forest (E-FRF) is proposed and justified theoretically from the consistency perspective. Our extensive experimental results indicate that the proposed method E-FRF has at least comparable performance to the comparative methods, and is an advantageous alternative to FRF in terms of both testing accuracy and running speed in most of the adopted high dimensional datasets.