The nonparametric evidential regression (EVREG) method provides flexible forms of prediction regarding the value of output, allowing the output of training instances to be partially unknown. However, the superfluous training instances still have negative effects on the parameter learning in EVREG. To relax this limitation, this paper introduces an interactive nonparametric evidential regression (IEVREG) algorithm with instance selection. More specifically, the significance of an instance is firstly measured by defining the evaluation functions, taking into account both the prediction accuracy of regression model and the spatial information between that instance with other ones. According to a search strategy, the instances with high degree of significance are then selected to maximize an objective function. Different from existing instance selection methods, the selection of training instances is synchronously accomplished with the parameter learning in IEVREG, rather than just a separated data preprocessing operation as traditional methods do. Furthermore, the noise and redundant instances can be simultaneously removed and the performance of IEVREG is robust to the order of presentation of instances in raw data set. Experimental results show that the proposed IEVREG algorithm has appropriate prediction accuracy, while performing well selection of the representative training instances from the raw data set. Simulations on synthetic and UCI real-world data sets validate our conclusions.
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The authors would like to thank the editors and anonymous referees for their invaluable comments and suggestions. This work is supported in part by the National Natural Science Foundation of China under Grants 51876035 and 51976032.
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Gong, C., Wang, P. & Su, Z. An interactive nonparametric evidential regression algorithm with instance selection. Soft Comput (2020) doi:10.1007/s00500-020-04667-4
- Nonparametric evidential regression
- Belief functions
- Instance selection