Comparison of different input selection algorithms in neuro-fuzzy modeling

Data driven neuro-fuzzy systems modeling requires the application of a suitable input selection method to identify the most relevant input variables. In view of the substantial number of existing input selection algorithms applied in neuro-fuzzy modeling, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situations. In this paper, we analyze the performance of five fundamental and widely used input selection algorithms, which encompass both model-free methods and model-based methods. Each of these algorithms is discussed in detail, and thus, present a comprehensive comparative analysis. Finally, we compare the performances of these algorithms by applying in stock price prediction problem. The experiments and the results provide a precious insight about the advantages and drawbacks of these five input selection algorithms.