Floating search methods in feature selection
Pattern Recognition Letters
Ant algorithms for discrete optimization
Artificial Life
Rapid and brief communication: Center-based nearest neighbor classifier
Pattern Recognition
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
Two step ant colony system to solve the feature selection problem
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Artificial life feature selection techniques for prostrate cancer diagnosis using TRUS images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Feature selection for classification of oscillating time series
Expert Systems: The Journal of Knowledge Engineering
Large margin mixture of AR models for time series classification
Applied Soft Computing
Efficient ant colony optimization for image feature selection
Signal Processing
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Feature extraction and feature selection are two important issues in sensor-based condition monitoring of any engineering systems. In this study, acoustic emission signals were first collected during grinding operations, next processed by autoregressive modeling or discrete wavelet decomposition for feature extraction, and then the best feature subsets are found by three different feature selection methods, including two proposed ant colony optimization (ACO)-based method and the famous sequential forward floating selection method. Posing monitoring as a classification problem, the evaluation is carried out by the wrapper approach with four different algorithms serving as the classifier. Empirical test results were shown to illustrate the effectiveness of feature extraction and feature selection methods.