Multiple Reducts Computation in Rough Sets with applications to Ensemble Classification

EasyChair Preprint 5064

Abstract

Rough sets have evolved as an important soft-computing paradigm for feature subset selection(Reduct computation) in the decision system amidst incompleteness and inconsistency. Multiple reducts computation using rough sets provide an elegant way for construction of ensemble classifier for better and stable classification. The existing approaches for multiple reducts computation are primarily based on the genetic algorithm and select diverse multiple reducts after generation of abundant candidate reducts. This work proposes an MRGA_MRC algorithm for multiple reducts computation by utilizing systematically evolving search space of all reducts computations in MRGA algorithm without generation of many candidate reducts. A novel heuristic is introduced for selection of diverse multiple reducts. Experiments conducted on the benchmark decision systems have established the relevance of the proposed approach in comparison to the genetic algorithm based multiple reducts computation approach REUCS.

Keyphrases: Multiple reducts computation, Reducts, discernibility matrix, ensemble classification, rough sets

@booklet{EasyChair:5064,
  author    = {Abhimanyu Bar and P. S. V. S. Sai Prasad},
  title     = {Multiple Reducts Computation in Rough Sets with applications to Ensemble Classification},
  howpublished = {EasyChair Preprint 5064},
  year      = {EasyChair, 2021}}