Photo by garri from unsplash
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,… Click to show full 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.
Journal Title: IEEE Transactions on Fuzzy Systems
Year Published: 2020
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!