A rough-GA hybrid algorithm for rule extraction from large data

The process of knowledge discovery from vast real life data is encountered with varieties of problems like, presence of noise and outliers in the data set, selection of proper subset of attributes (features) from a large number of relevant and irrelevant attributes, fuzzification or discretization of real-valued data, and finally rule induction. In this proposal, the process of rule creation has two steps. The first step consists of attribute selection, which is based on rough set theory. The next phase is to explore optimal set of simple yet accurate rules. This is accomplished by genetic algorithm. Here, the contribution is how to set the fitness of chromosomes so that simplicity-accuracy tradeoff is accomplished. Finally, chromosomes are coalesced to further simplify and reduce the number of rules.