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Academic Journal of Computing & Information Science, 2024, 7(3); doi: 10.25236/AJCIS.2024.070302.

Collaborative Reduction of Features and Instances in the Set-valued Decision System


Ruimin Li

Corresponding Author:
Ruimin Li

School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo City, China


Due to the advancement of technology, data is becoming richer in features and instances, but not all features and instances help to improve classification performance in data mining. Data reduction helps to alleviate the difficulty of learning techniques when the data is large, and rough sets have been widely used for data reduction. Semi-monolayer covering rough set is an efficient and high-quality rough set model in set-valued information systems. In this paper, a new data reduction scheme is proposed from the perspective of incremental updating of semi-monolayer covering rough set (abbr. FSMCDE). Firstly, in the set-valued decision system, based on the fact that the lower approximation set gradually increases with the features until it remains stable, the limit for the lower approximation set of semi-monolayer covering rough set to remain stable is proved, and the incremental updating theory of the lower approximation set is designed. Secondly, the features are continuously added to the set-valued system, and the incremental algorithm is used to update the lower approximation set until it reaches the limit, completing the collaborative reduction of features and instances. Furthermore, to reduce the blindness of adding features during incremental updating, Fisher score is introduced to form the final collaborative reduction algorithm of features and instances. The experimental results show that FSMCDE can efficiently accomplish the collaborative reduction of features and instances, and effectively improve the classification performance.


Set-valued information system; Semi-monolayer covering; Incremental updating; Collaborative reduction; Fisher Score

Cite This Paper

Ruimin Li. Collaborative Reduction of Features and Instances in the Set-valued Decision System. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 3: 13-20. https://doi.org/10.25236/AJCIS.2024.070302.


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