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

An enhanced KNN-based twin support vector machine with stable learning rules
عنوان نشریه: 
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دوره: 
۳۲
شماره: 
۱۶
شماره صفحه (از - تا): 
۱۲۹۴۹-۱۲۹۶۹
چکیده

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.‎

استناد: 

Nasiri, Jalal A.‎, and Amirmahmoud Mir.‎ 2020.‎ An enhanced KNN-based twin support vector machine with stable learning rules.‎ Neural Computing and Applications ۳۲ (۱۶): ۱۲۹۴۹-۱۲۹۶۹.

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