Minimum Variance Embedded Intuitionistic Fuzzy Weighted Random Vector Functional Link Network

Abstract

Randomized neural networks such as random vector functional link network have been successfully employed in regression and classification problems. In real world, most of the data contaminated by outliers and noisy samples and hence, intelligent models are needed to classify such data. To make the model robust to noise and incorporate the geometric structure of the data, we propose minimum variance intuitionistic fuzzy RVFL (MVIFRVFL) model. In the proposed MVIFRVFL model, each sample is assigned an intuitionistic fuzzy number that is defined based on membership and non membership values. Membership value of a sample is considered according to its distance from the class center and the nonmembership value is assigned to each sample based on the ratio of the number of heterogeneous points to the total number of points in its neighborhood. Moreover, we incorporate the geometric aspect of the data, we minimise the variance among the data points of each class to improve the generalization performance. The performance of the classification models are analysed on 33 datasets taken from UCI and KEEL repository. From the experimental results, it is evident that the proposed MVIFRVFL model outperformed the state-of-the-art algorithms with best accuracy, highest number of wins among all the datasets and grabbed lowest rank amongst the baseline models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.