Non-persistent stratified sampling based IQRA_IG for scalable reduct generation

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.

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