A Summary on DMU MSCA project GS-A-DM-DS(629051)
The EU-funded DMU Marie (Skłodowska) Curie Actions project: Grey Systems and Its Application to Data Mining and Decision Support (GS-A-DM-DS, 629051) was completed at the end of 2016. The GS-A-DM-DS team proposed several new prediction and decision-making models to provide more reliable results in complex situations.
They formulated a set of criteria for grey model selection and calibration following systematic research. This will assist in making grey prediction and forecasting easily accessible to new users who have no prior knowledge in grey systems. The criteria will also help to promote the application of grey systems to data mining in Europe.
Project partners developed several grey models in order to achieve more accurate and reliable prediction and forecasting with “small data” and poor information. These will contribute to data mining operations that require high speed and reliability while reducing data requirements. They proposed the even difference model GM(1,1) (EDGM), the original difference model GM(1,1) (ODGM), self-memory grey model, and fractional order grey models, etc.
Researchers also developed decision-making models that were validated by simulation and real application case studies. Performance was superior to existing alternatives. These models will enable more realistic and reliable decision-making, and help ensure the uncertainty representation is more accessible for ordinary users.
One such example is the new grey clustering evaluation model, based on mixed possibility functions, which includes both end-point mixed possibility functions and centre-point mixed possibility functions. It’s easy to obtain the possibility functions and solve the evaluation problems of uncertain systems with poor information. They also studied the problems of multi-attribute intelligent grey target decision-making, and then constructed four kinds of uniform effect measure functions in view of the different decision-making objectives based on benefit type, cost type, and moderate type.
Accordingly, the various decision-making objectives which possess different meanings, dimensions, and/or nature from each other can now be transferred and measured to uniform effect. The critical value of a grey target is designed as the dividing point between positive and negative, which is defined as zero. The objective effect values were fully considered, and as a result, a new multi-attribute intelligent grey target decision-making model was proposed. Dealing with the decision-making dilemma of a comparison between the maximum components of two decision coefficient vectors is different from comparisons between the two integrated decision coefficient vectors themselves. Therefore, both the weight vector group of kernel clustering and weighted coefficient vectors of kernel clustering for decision-making were firstly defined. A novel two-stage decision-making model with the weight vector group of kernel clustering and weighted coefficient vector of kernel clustering for decision-making was then put forward. This method can effectively solve the decision-making dilemma and produce consistent results.
Over 30 research papers were presented in leading international academic journals and conferences. In addition, more than 10 visits, seminars and training courses were carried out in China and Europe. As a result, an international association on grey systems and uncertainty analysis was established comprising members from China, Europe and North America. Furthermore, the MSCA fellow of the DMU MSCA project GS-A-DM-DS(629051) has been selected as one of the 10 shortlisted promising scientists in the category ‘Communicating Science’ of the MSCA 2017 Prizes.
GS-A-DM-DS has demonstrated the feasibility of grey systems in data mining and its great potential for use with limited and poor data. It will have a significant impact on the development of grey systems and data mining in Europe.