Input selection is an important step in nonlinear regression modeling. By input selection, an interpretable model can be built with less computational cost. Input selection thus has drawn great attention in recent years. However, most available input selection methods are model-based. In this case, the input data selection is insensitive to changes. In this paper, an effective model-free method is proposed for the input selection. This method is based on sensitivity analysis using Minimum Cluster Volume (MCV) algorithm. The advantage of our proposed method is that with no specific model needed to be built in advance for checking possible input combinations, the computational cost is reduced and changes of data patterns can be captured automatically. The effectiveness of the proposed method is evaluated by using three well-known benchmark problems, which show that the proposed method works effectively with small and medium sized data collections. With an input selection procedure, a concise fuzzy model is constructed with high accuracy of prediction and better interpretation of data, which serves the purpose of patterns discovery in data mining well.
Modeling Dynamic User Interests: A Neural Matrix Factorization Approach
