Developments and Evaluation of Twin Learning Algorithms: A Systematic Review

This chapter contributes a detailed study on the developments of twin learning algorithms that are happened on top of the Twin Support Vector Machine (TWSVM), Twin Extreme Learning Machine (TELM) and Twin Random Vector Functional Link (TRVFL). Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. According to the demands of the digital age, machine learning algorithms have made significant strides. Twin Algorithms' level of performance ought to be superior to that of its parents'. The improvements in the running time of TELM and TWSVM are commendable and have given confidence to the researchers. The twin models that followed Extreme Learning Machine (ELM) and the single-hidden-layer learning paradigms both attempted to overcome a number of their parents' shortcomings. Artificial intelligence will advance thanks to iterative single-hidden-layer models and their reliable performances. In this chapter, some of the works in twin learning algorithms that are either technically sound or better in the performances are taken for the study. The recent developments in twin algorithms, especially in the single hidden layered models, found more attractive because of the underlined learning procedure than the performance. The working principle and performance of the algorithms are detailed with the help of published works and findings.