An enhanced KNN-based twin support vector machine with stable learning rules

TL;DR

This paper introduces RKNN-TSVM, an improved twin support vector machine that reduces noise sensitivity, stabilizes learning, and significantly decreases computational time using a novel KNN-based approach.

Contribution

It proposes a regularized KNN-based twin support vector machine with weighted samples, added stabilizer, and an efficient KNN algorithm to enhance accuracy and speed.

Findings

Achieves higher classification accuracy on benchmark datasets.

Reduces computational time by up to 14 times.

Demonstrates robustness against noise and outliers.

Abstract

Among the extensions of twin support vector machine (TSVM), some scholars have utilized K-nearest neighbor (KNN) graph to enhance TSVM's classification accuracy. However, these KNN-based TSVM classifiers have two major issues such as high computational cost and overfitting. In order to address these issues, this paper presents an enhanced regularized K-nearest neighbor based twin support vector machine (RKNN-TSVM). It has three additional advantages: (1) Weight is given to each sample by considering the distance from its nearest neighbors. This further reduces the effect of noise and outliers on the output model. (2) An extra stabilizer term was added to each objective function. As a result, the learning rules of the proposed method are stable. (3) To reduce the computational cost of finding KNNs for all the samples, location difference of multiple distances based k-nearest neighbors algorithm (LDMDBA) was embedded into the learning process of the proposed method. The extensive experimental results on several synthetic and benchmark datasets show the effectiveness of our proposed RKNN-TSVM in both classification accuracy and computational time. Moreover, the largest speedup in the proposed method reaches to 14 times.