Liang, B.; Jin, E.; Wei, L.; Hu, R. Knowledge Granularity Attribute Reduction Algorithm for Incomplete Systems in a Clustering Context. Mathematics2024, 12, 333.
Liang, B.; Jin, E.; Wei, L.; Hu, R. Knowledge Granularity Attribute Reduction Algorithm for Incomplete Systems in a Clustering Context. Mathematics 2024, 12, 333.
Liang, B.; Jin, E.; Wei, L.; Hu, R. Knowledge Granularity Attribute Reduction Algorithm for Incomplete Systems in a Clustering Context. Mathematics2024, 12, 333.
Liang, B.; Jin, E.; Wei, L.; Hu, R. Knowledge Granularity Attribute Reduction Algorithm for Incomplete Systems in a Clustering Context. Mathematics 2024, 12, 333.
Abstract
The phenomenon of missing data can be seen everywhere in reality. Most typical attribute reduction models are only suitable for complete systems. But for incomplete systems, we can’t obtain the effective reduction rules. Even if there are a few reduction approaches, the classification accuracy of their reduction sets still needs to be improved. In order to overcome these shortcomings, this paper firstly defines the similarities of intra-cluster objects and inter-cluster objects based on the tolerance principle and the mechanism of knowledge granularity. Secondly, attributes are selected on the principle that the similarity of inter-cluster objects is small and the similarity of intra-cluster objects is large, and then the knowledge granularity attribute model is proposed under the background of clustering; Then, the IKAR algorithm program is designed. Finally, a series of comparative experiments about reduction size, running time and classification accuracy are conducted with twelve UCI data sets to evaluate the performance of IKAR algorithms, then the stability of Friedman test and Bonferroni-Dunn test are conducted. The experimental results indicate that the proposed algorithms are efficient and feasible.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
