Expert Systems with Applications: An International Journal
A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis
Expert Systems with Applications: An International Journal
Ovarian cancer diagnosis with complementary learning fuzzy neural network
Artificial Intelligence in Medicine
KCMAC: A Novel Fuzzy Cerebellar Model for Medical Decision Support
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
A prediction algorithm for time series based on adaptive model selection
Expert Systems with Applications: An International Journal
A novel brain-inspired neural cognitive approach to SARS thermal image analysis
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
A new dataset evaluation method based on category overlap
Computers in Biology and Medicine
Cultural dependency analysis for understanding speech emotion
Expert Systems with Applications: An International Journal
A novel brain-inspired neuro-fuzzy hybrid system for artificial ventilation modeling
Expert Systems with Applications: An International Journal
SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System
Expert Systems with Applications: An International Journal
Using Hilbert scan on statistical color space partitioning
Computers and Electrical Engineering
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Most recent research efforts on feature selection have focused mainly on classification task due to its popularity in the data-mining community. However, feature selection research in nonlinear system estimations has been very limited. Hence, it is reasonable to devise a feature selection approach that is computationally efficient on nonlinear system estimations context. A novel feature selection approach, the Monte Carlo evaluative selection (MCES), is proposed in this paper. MCES is an objective sampling method that derives a better estimation of the relevancy measure. The algorithm is objectively designed to be applicable to both classification and nonlinear regressive tasks. The MCES method has been demonstrated to perform well with four sets of experiments, consisting of two classification and two regressive tasks. The results demonstrate that the MCES method has following strong advantages: 1) ability to identify correlated and irrelevant features based on weight ranking, 2) application to both nonlinear system estimation and classification tasks, and 3) independence of the underlying induction algorithms used to derive the performance measures