The theory of rough sets is an extension of set theory for studying intelligent systems characterized by insufficient and incomplete information. We discuss the basic concept and properties of knowledge reduction based on inclusion degree and evidence reasoning theory, and propose a knowledge discovery approach based on inclusion degree and evidence reasoning theory.
Knowledge management in Enterprise Information Systems (EIS) has become one of the hottest research topics in the last few years. Operations Support Systems (OSS) is one kind of EIS, which is becoming increasingly popular in the telecommunications industry. However, the academic research on knowledge management in OSS is sparse. In this paper, a knowledge management system for OSS is proposed in the framework of systems theory. Knowledge, knowledge management, organization and information technology are the four main interactive elements in the knowledge management system. The paper proposes that each subsystem of the OSS is to be equipped with knowledge management capacity, and the knowledge management of the OSS is to be realized through its subsystems. * Customer care: provide an interface to the customers for all issues related to customer order, sales, billing, and problem handling. * Multi-service provision: activate instances of service for particular customers.
In this paper, we propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet®, a model which won the Gold Prize in the 2003 mobile customer churn prediction modeling contest (The Duke/NCR Teradata Churn Modeling Tournament). In fact, ADTreesLogit has better predictive accuracy than TreeNet® on two important observation points.
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