Abstract
Traditionally, the shortest length has been used as the optimality criterion in rough set based optimal / near-optimal reduct computation. A more generalizable alternative to the optimal reduct computation approach was recently introduced, with the coarsest granular space as the optimality criterion. However, owing to exponential time complexity, it is not scalable to even moderate-sized data sets. This article investigates to formulate two near-optimal reduct computation alternatives for scaling comparatively larger data sets. The first algorithm employs a controlled A∗ search based strategy to find a near-optimal reduct while reducing both space utilization and computational time. Whereas, the second algorithm employs a greedy sequential backward elimination (SBE) strategy on the higher granular space attribute ordering for achieving coarsest granular space based near-optimal reduct. The comparative experimental study is conducted among the proposed approaches with the coarsest granular space based optimal reduct algorithm A∗RSOR and state-of-the-art shortest length based optimal and near-optimal reduct algorithms. The experimental study amply validates the relevance of the proposed approaches in obtaining near-optimal reduct with increased scalability and comparable or improved generalizable classification models induction.
