Attribute Reduction using Hybrid Extraction

The major problems facing the decision making is how to handle uncertain data, several theories are dealing with uncertainty, soft set theory also handle this uncertainty problem. Reduction techniques are still an open area to be explored in knowledge management, which focuses on uncertain data removing unlike comparisons. This paper proposes based on rough set theory and soft set theory, it deletes the parameter, then execute Hybrid reduction technique which known as Hybrid Extraction for generating optimal, sub optimal until last optimal result. As part of this analysis, a comparison test with previous reduction techniques. The conclusion part the Hybrid Extraction shows better alternative results compared to Hybrid reduction and normal parameter reduction in terms of efficiency and response time.