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A wide range of classification methods have been used for the early detection of financial risks in recent years. How to select an adequate classifier (or set of classifiers) for a given dataset is an important task in financial risk prediction. Previous studies indicate that classifiers’ performances in financial risk prediction may vary using different(More)
The evaluation of clustering algorithms is intrinsically difficult because of the lack of objective measures. Since the evaluation of clustering algorithms normally involves multiple criteria, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper presents an MCDM-based approach to rank a selection of popular clustering(More)
Nowadays, with increasingly intense competition in the market, major banks pay more attention on customer relationship management. A real-time and effective credit card holders’ churn analysis is important and helpful for bankers to maintain credit card holders. In this research we apply 12 classification algorithms in a real-life credit card(More)
Although software reliability can be evaluated by applying data mining techniques in software engineering data to identify software defects or faults, it is difficult to select the best algorithm among the numerous data mining techniques. The goal of this paper is to propose a multiple criteria decision making (MCDM) framework for data mining algorithms(More)
One of the biggest challenge in emergency management is how to deal with the incomplete, contradict and fuzzy information. This paper reviews related work and develop a framework for heterogeneous information integration in emergency management. A high-level data integration module in which heterogeneous data sources are integrated and presented in a(More)
Market segmentation is one of the most important areas of knowledge-based marketing. When it comes to personal financial services in retail banks, it is really a challenging task as data bases are large and multidimensional. The conventional ways in customer segmentation are knowledge based and often get bias results. On the contrary, data mining can deal(More)