Web Service Based Feature Selection and Discretization with Efficiency

Web service is an emerging technology that enables the users to access heterogeneous, distributed resources, providing easier integration and interoperability between data and its applications. Big data analysis is the one of the major problems in web based machine learning and data mining. Big data contain high degree of irrelevant and redundant information´s which are greatly degrades the performance of learning algorithms. Therefore, feature selection becomes necessary for machine learning tasks for facing high dimensional data. Discretization turns continuous attributes into discrete ones by dividing the values into small number of intervals. In this paper, a new web service based feature selection and discretization method called NANO is presented. The proposed web service method was tested with reputed datasets, it shows high classification accuracy and improves the computation efficiency.