An efficient angle-based twin random vector functional link classifier

doi:10.1016/j.asoc.2024.112005

2024-10-01

PRE

Deepak Gupta *

Barenya Bikash Hazarika

DOI：10.1016/j.asoc.2024.112005

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摘要

En 中文

Random vector functional link (RVFL) has always proven to be an excellent classifier in various application areas of machine learning. In this work, inspired by RVFL and its twin variant, i.e., twin RVFL (TRVFL), we propose an efficient angle-based twin random vector functional link (ATRVFL) to address the binary classification problem. ATRVFL is a general classification model in which the first optimization problem can be found by solving a quadratic programming problem (QPP). The second problem can be described as an unconstrained minimization problem (UMP) which is solved by a system of linear equations. ATRVFL finds the solution by solving a QPP and a UMP instead of solving two QPPs. Therefore, its training time is significantly less than TRVFL. To maximize the angle between the normals of the two hyperplanes, the second hyperplane is chosen to be close to its class. The objective of introducing the idea of angle is to maximise the distance between two hyperplanes to improve the ability to discriminate them in geometric spaces. Experimental analyses are performed on a leaf dataset and 25 real-world benchmark datasets (RWDs) collected from the UCI repository of various fields like medical, biological, etc. The results are evaluated based on classification accuracy, the area under the curve, G-mean and F1score. The results of ATRVFL are compared with Support Vector Machine (SVM), RVFL, Twin-RVFL (TRVFL), RVFL with epsilon-insensitive Huber loss (epsilon-HRVFL), ensemble deep learning-based RVFL network (edRVFL) and Intuitionistic fuzzy random vector functional link classifier (IFRVFLC). The classification accuracy of the proposed model on the leaf dataset is 91.093 % and the highest F1-score of 0.768. Overall, based on experimental analysis it is evident that the proposed ATRVFL shows comparative or better performance than SVM, RVFL, TRVFL, epsilon-HRVFL, IFRVFLC and edRVFL.

Keyword:

Binary classification

Class imbalance learning

RVFL

Angle-based twin RVFL