Aggregate system analysis for prediction of tardiness and mixed zones of continuous casting with fuzzy methodology
Pharmacokinetic application of fuzzy structure identification and reasoning
Information Sciences: an International Journal - Special issue: Medical expert systems
A cluster validation index for GK cluster analysis based on relative degree of sharing
Information Sciences—Informatics and Computer Science: An International Journal
Genetic polynomial regression as input selection algorithm for non-linear identification
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Review: Expert systems and evolutionary computing for financial investing: A review
Expert Systems with Applications: An International Journal
A type-2 fuzzy rule-based expert system model for stock price analysis
Expert Systems with Applications: An International Journal
Two cooperative ant colonies for feature selection using fuzzy models
Expert Systems with Applications: An International Journal
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
Expert Systems with Applications: An International Journal
Knowledge discovery by a neuro-fuzzy modeling framework
Fuzzy Sets and Systems
Input selection in learning systems: a brief review of some important issues and recent developments
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Expert Systems with Applications: An International Journal
Input selection for nonlinear regression models
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Application of adaptive network based fuzzy inference system method in economic welfare
Knowledge-Based Systems
Hi-index | 12.05 |
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.