Incremental concept cognitive learning based on three-way partial order structure

With the vigorous development of the information technology industry, the information data available to mankind has shown an explosive growth trend. Dynamic concept learning is an approach that can effectively process the acquired massive data and extract valuable information from them. Concept cognitive learning (CCL) is a very active research direction in the field of dynamic concept learning, while partial order formal structure analysis (POFSA) is a concrete and practical model of CCL. However, the existing CCL algorithms in POFSA face some challenges when processing constantly changing data. Therefore, this paper is devoted to explore an incremental CCL algorithm based on three-way object partial order structure diagram (OPOSD) in POFSA with the incorporation of the thoughts of incremental learning. The features of five object categories are considered, and their incremental influences on three-way OPOSD are analyzed and their incremental CCL algorithms in three-way OPOSD are established. Based on some real famous formal contexts, this paper conducts numerical experiments, and the results show that the incremental CCL algorithm based on three-way OPOSD is consistent with human cognitive principles, and can improve the CCL performance of POFSA as well. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper discusses the application and experimental results of the incremental concept cognitive learning algorithm based on three-way object partial order structure diagram in dynamic concept learning, demonstrating its compliance with human cognitive principles and performance enhancement.