An improved adaptive neuro-fuzzy inference system using probability trajectory-based clustering ensemble

Research findings established the usefulness of Adaptive neuro-fuzzy inference system (ANFIS) in predicting outcomes of events, processes or systems from their numerical representations (datasets). However, its effectiveness in terms of prediction accuracy and complexity depends on some factors specifically its set of rules that models data behavior, and tune-able parameters (antecedent, consequent and rule base). Although several promising non-heuristic and meta-heuristic optimization techniques like gradient descent, Ant Colony, Genetic Algorithm, Particle Swarm, and Invasive Weed have been proposed to improve on the prediction accuracy of ANFIS through proper optimization of its tune-able parameters, but absolutely no work has applied clustering ensemble to improving on ANFIS for better accuracy especially on rules improvement. Therefore, in this paper, we propose an improved ANFIS that uses an agglomerative-based clustering ensemble of fuzzy c-means to help extract rules from a given dataset that represent a process, event or system. The ensemble uses probability trajectories of random walk processes on base clustering partitions of the dataset so as to refine direct co-association relationships or links among data objects of the base partitions thereby improving on these relationships that later metamorphosed to fuzzy rules and finally, a better accuracy. Comparative analysis of our proposed improved ANFIS alongside with the conventional ANFIS using two standard petrophysical datasets for lithology prediction shows that the proposed ANFIS is better than the conventional ANFIS in terms of prediction model accuracy and stability although, both maintain the same architecture in terms of the number of rules and tune-able parameters as well as the training methods. (C) 2020 Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative.