Prediction of Caesarian Possiblities with Rough Set Theory and Fuzzy Petrinet

Abstract

The Rough set theory (RST) is a method proved its efficiency and simplicity in machine learning and successfully developing now a day’s vastly and rapidly. Many extensions and hybrid algorithms are proposed so far which pavement solutions for many mining issues. In this paper the performance of RST with Fuzzy Petri Nets (FPN) is studied in the sense how it carried out in the process of data mining and predicting rules. The Rough set theory is well known for its simplicity in attribute reduction and Fuzzy Petri Nets is very much used for rule chaining mechanism. This can capture the concurrency and choices of rules. This paper experiments the caesarian data to investigate the decision making from the rules generated by LERS system of RST with the approach of FPN. The possibilities of prediction and their consequences based on the accuracy of the classifications were discussed.