Parallel implementation of inference process in fuzzy rule-based classifiers using GPGPUs

This paper presents a parallel implementation of fuzzy-rule-based classifiers using a GPGPU (General Purpose Graphics Processing Unit). There are two steps in the process of fuzzy rule-based classification: Fuzzy-rule generation from training data and classification of an unseen input pattern. The proposed implementation parallelizes these steps. In the step of fuzzy-rule generation from training patterns, the membership calculation of a training pattern for available fuzzy sets is simultaneously processed. On the other hand, the membership calculation of an unseen pattern for the generated fuzzy if-then rules is simultaneously processed in the step of the classification of the pattern. The efficiency of the parallelization is evaluated through a series of computational experiments. Three data sets of microarray expression are used to diagnose colon cancer, leukemia, and lymphoma. The results of the computational experiments show that the proposed implementation successfully improve the speed of fuzzy rule-based classifiers.