Age Prediction and Performance Comparison by Adaptive Network based Fuzzy Inference System using Subtractive Clustering

Abstract

To integrate the best features of fuzzy systems and neural networks, a data mining approach with ANFIS is applied on all features of Abalone and Monk’s problem dataset. The main aim of this research is to reduce the RMSE with fewer numbers of rules in order to achieve high speed and less time consumed in both learning and application phases. For calculating effective RMSE, an adaptive fuzzy inference system with subtractive clustering is proposed. Effective partition of input space is done and loaded into the ANFIS editor. A fuzzy inference system is generated using subtractive clustering and RMSE of training and testing is calculated by hybrid approach which is combination of back propagation and least square method. A structure is generated which shows input and output data along with number of fuzzy rules. This result into lower RMSE with fewer numbers of rules shows that ANFIS is well suited for age prediction of abalone and performance comparison of learning algorithms.