Modification and complexity analysis of an incremental learning algorithm under the VPRS model

Abstract

This article introduced the modification of an incremental learning algorithm and summarized its performance via the complexity analysis. The algorithm was originally proposed in the context of classic rough set theory, utilizing the hierarchy of probabilistic decision tables as the classifier. The variable precision rough set model (VPRS model) is an extension of the classic rough set theory with unique features. When implemented under the VPRS model, the algorithm has to be modified; for example, some of its strategies can be merged and additional operations are required. Initially, the algorithm was modified into a version specifically suitable for the field of face recognition. This article further reformulated the algorithm so that it can be potentially applied in different areas and, after that, analyzed its complexity.