Fuzzy quasi-linear SVM classifier: Design and analysis

Multiple Support Vector Machine (SVM) classifier based on ensemble learning approaches could be enhanced from the view point of accuracy, but the performance of these classifiers closely depends on the initial condition of the partitioning method used in the design. Furthermore, these classifiers are more easily affected by noise and outliers. In this study, a novel fuzzy quasi linear SVM classifier realized with the aid of a composite kernel function and Fuzzy C-Means (FCM) clustering is proposed. The objective of this approach is to reduce the effect of noise and outliers and also handle the overfitting problem through the synergistic effect of the two methods: First, Fuzzy C-Means (FCM) is used to partition the training dataset into several subsets as a preprocessing phase of the proposed classifier. Second, the composite kernel based on multiple linear kernel expression is considered to avoid overfitting problem. In more detail, each training data is assigned to the corresponding membership degree. Some data which are potential noise or outliers are assigned with lower membership degrees and thus yield a small contribution to the composite kernel function. Then, the composite kernel function for multiple local SVMs is constructed according to the distribution of training data. The designed fuzzy quasi-linear SVM classifier is tested with both artificial and UCI data sets. It is also applied for sorting the problem of black plastic wastes being handled in the practice in order to verify the effective as well as efficient classification improvement. Experimental results demonstrate that the proposed method shows the elevated classification performance when compared to performance produced by the methods studied previously. ? 2020 Elsevier B.V. All rights reserved.

Automated summary

This study introduces a novel fuzzy quasi linear SVM classifier based on ensemble learning methods and composite kernel function, aiming to reduce the impact of noise and outliers, as well as address overfitting issues. Experimental results demonstrate that this approach exhibits superior classification performance.