TY - JOUR
T1 - Novel Incremental Algorithms for Attribute Reduction from Dynamic Decision Tables Using Hybrid Filter-Wrapper with Fuzzy Partition Distance
AU - Giang, Nguyen Long
AU - Son, Le Hoang
AU - Ngan, Tran Thi
AU - Tuan, Tran Manh
AU - Phuong, Ho Thi
AU - Abdel-Basset, Mohamed
AU - De Macedo, Antonio Roberto L.
AU - De Albuquerque, Victor Hugo C.
N1 - Funding Information:
Manuscript received March 6, 2019; revised May 22, 2019, August 9, 2019, September 30, 2019, and October 4, 2019; accepted October 8, 2019. Date of publication October 22, 2019; date of current version May 4, 2020. This work was supported by the Research Project, VAST 01.05/19-20, Vietnam Academy of Science and Technology. (Corresponding authors: Le Hoang Son; Mohamed Abdel-Basset.) N. L. Giang is with the Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi 100000, Vietnam (e-mail: [email protected]).
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
KW - Attribute reduction
KW - decision tables
KW - fuzzy distances
KW - fuzzy rough sets
KW - incremental algorithms
UR - http://www.scopus.com/inward/record.url?scp=85078754445&partnerID=8YFLogxK
U2 - 10.1109/TFUZZ.2019.2948586
DO - 10.1109/TFUZZ.2019.2948586
M3 - Article
AN - SCOPUS:85078754445
SN - 1063-6706
VL - 28
SP - 858
EP - 873
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 5
M1 - 8879615
ER -