Novel Incremental Algorithms for Attribute Reduction from Dynamic Decision Tables Using Hybrid Filter-Wrapper with Fuzzy Partition Distance

Nguyen Long Giang, Le Hoang Son, Tran Thi Ngan, Tran Manh Tuan, Ho Thi Phuong, Mohamed Abdel-Basset, Antonio Roberto L. De Macedo, Victor Hugo C. De Albuquerque

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

Attribute reduction from decision tables has been much focused in recent years in which the incremental methods of the tradition rough set and extended models are mostly used for adding, removing, or updating the object or attribute set. However, when dealing with the dynamic decision tables, the existing incremental methods do not recalculate information which has been added into the decision table. In this article, we propose some new incremental methods using the hybrid filter-wrapper with fuzzy partition distance on fuzzy rough set. Experimental results indicate that the proposed algorithms decrease significantly the cardinality of reduct as well as achieve higher accuracy than the other filter incremental methods such as IV-FS-FRS-2, IARM, ASS-IAR, IFSA, and IFSD.

Original languageEnglish
Article number8879615
Pages (from-to)858-873
Number of pages16
JournalIEEE Transactions on Fuzzy Systems
Volume28
Issue number5
DOIs
Publication statusPublished - May 2020
Externally publishedYes

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