MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections

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 t...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2007-03, Vol.18 (2), p.431-448
Hauptverfasser:	Kian Hong Quah, Quek, C.
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy measure adaptive neurofuzzy inference systems (ANFISs) Adaptive systems Algorithm design and analysis Algorithms Artificial Intelligence audio signal classification automobile miles per gallon (MPG) prediction Automobiles Classification Computational efficiency Computer Simulation Data mining Decision trees Dynamical systems Filters financial data modeling induction algorithm Inference algorithms Information Storage and Retrieval - methods low computational cost Models, Statistical Monte Carlo evaluative feature selection Monte Carlo evaluative selection (MCES) Monte Carlo Method Monte Carlo methods multilayer perceptron (MLP) Neural networks Neural Networks (Computer) Nonlinear dynamics Nonlinear systems Pattern classification Pattern Recognition, Automated - methods regressive series reinforcement learning Sampling methods Studies Tasks
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