Heterogeneous data reduction model for payment request file of direct debit processes

This paper presents a proposed model regarding Heterogeneous Data Reduction. The model reduces data over a heterogeneous environment through feature selection/extraction. The feature is selected/extracted directly from its data source and prepared without an initial integration for all data sources. After that the selected/extracted prepared feature is integrated into a new reduced data set Feature selection/extraction is made according to business requirements, domain expert feedbacks, and the organization's Service Level Agreement and Corporate Household to give high accuracy results. The proposed model is built by hybrid data reduction techniques: Stepwise Backward Elimination, Stepwise Forward Selection and Decision Tree Induction. Such proposed model building depends on the CRoss Industry Standard Process model of data mining as a reference model The proposed model works with any kind of data types. The model applies to real telecommunication data relating to the Direct Debit processes. It is used to produce a Standard Converted Reduced Payment Request File to just keep on the important attributes. The model helps to cut down the user work time to generate that new data set