A Hybrid Fuzzy C-Means Clustering with Artificial Fish Swarm Algorithm Optimization for Data Mining with Cascade RMC-FSVM

Abstract

Different endeavours have been made to enhance Support Vector Machines (SVMs) in light of various situations of certifiable issues. SVMs are the purported benchmarking neural system innovation propelled by the aftereffects of statistical learning theory. Among them, considering specialists' learning has been affirmed to enable SVMs to manage noisy data to acquire more helpful outcomes. For instance, SVMs with monotonicity constraints and with the Tikhonov regularization method, otherwise called Regularized Monotonic SVM (RMCSVM) consolidate imbalance requirements into SVMs in view of the monotonic property of real-world issues, and the Tikhonov regularization technique is additionally connected to guarantee that the arrangement is one of a kind and limited. These sorts of SVMs are additionally alluded to as knowledge-oriented SVMs. Be that as it may, explaining SVMs with monotonicity imperatives will require significantly more calculation than SVMs. In this exploration, at in the first place, Cascade RMC-FSVM can be spread over different processors with modified map decreased system over overhead and requires far less memory, deciding suitable noise or outlier is basic with traditional FSVMs, as these can impactsly affect the learning performance, and clustering functions can diminish the impacts of noise or outliers. This investigation proposed a Hybrid Fuzzy C-Means (HFCM) clustering with Artificial Fish School Algorithm (AFSA) strategies to observationally identify that damaging the monotonic condition in the training data can lessen the generalizability and monotonicity of RMC-FSVM. The consequently chosen virtual sets are performed in view of the technique of AFSA with least and most extreme of certain arbitrary subsets of the input dataset is considered as the fitness estimation of AFSA. The proposed work's judgment is done in the matlab simulation environment from which it is affirmed that the proposed examine technique tends to give a decent outcome when contrasted with the current methodologies, for the most part as for the precise knowledge learning.