Non-persistent stratified sampling based IQRA_IG for scalable reduct generation. (1st January 2014)

• Record Type: Journal Article
• Authors: Sai Prasad, P.S.V.S.; Rao, C. Raghavendra
• Is Part Of: International journal of granular computing, rough sets and intelligent systems. Volume 3:Number 3(2014)
• Page Start: 234
• Page End: 255
• Publication Date: 2014-01-01
• Subjects: rough sets -- reduct -- quick reduct -- scalability -- stratified sampling -- fidelity; Intelligent agents (Computer science) -- Periodicals; 006.3
• Languages: English
• ISSNs: 1757-2703

Abstract

Feature selections in large datasets using reduct based on rough set principles is computationally expensive. The existing scalable sampling-based reduct computation algorithms suffer from limitations like redundancy and inadequacy. This paper develops two algorithms for finding reduct based on stratified sampling and non-persistent stratified sampling techniques which addresses adequacy and to certain extent redundancy. This paper compares the performance of these algorithms against discernbility matrix-based sampling approximate reduct algorithm (SARA) and sample guided improved quick reduct algorithm with information gain heuristic (SGIQRA_IG). The performance of these algorithms is demonstrated on benchmark large dataset repository of Arizona State University.