Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Ant algorithms for discrete optimization
Artificial Life
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ant colony optimization theory: a survey
Theoretical Computer Science
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
Neural Computing and Applications
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
A Combined Ant Colony and Differential Evolution Feature Selection Algorithm
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Text feature selection using ant colony optimization
Expert Systems with Applications: An International Journal
Identifying core sets of discriminatory features using particle swarm optimization
Expert Systems with Applications: An International Journal
Ant colony and particle swarm optimization for financial classification problems
Expert Systems with Applications: An International Journal
A novel ACO-GA hybrid algorithm for feature selection in protein function prediction
Expert Systems with Applications: An International Journal
Classification of surface EMG signal using relative wavelet packet energy
Computer Methods and Programs in Biomedicine
Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis
Computer Methods and Programs in Biomedicine
A novel swarm based feature selection algorithm in multifunction myoelectric control
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Fuzzy-rough data reduction with ant colony optimization
Fuzzy Sets and Systems
Pitfalls of supervised feature selection
Bioinformatics
A novel pattern classification method for multivariate EMG signals using neural network
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Feature selection with particle swarms
CIS'04 Proceedings of the First international conference on Computational and Information Science
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Statistical analysis of the parameters of a neuro-genetic algorithm
IEEE Transactions on Neural Networks
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This paper presented a new ant colony optimization (ACO) feature selection method to classify hand motion surface electromyography (sEMG) signals. The multiple channels of sEMG recordings make the dimensionality of sEMG feature grow dramatically. It is known that the informative feature subset with small size is a precondition for the accurate and computationally efficient classification strategy. Therefore, this study proposed an ACO based feature selection scheme using the heuristic information measured by the minimum redundancy maximum relevance criterion (ACO-mRMR). The experiments were conducted on ten subjects with eight upper limb motions. Two feature sets, i.e., time domain features combined with autoregressive model coefficients (TDAR) and wavelet transform (WT) features, were extracted from the recorded sEMG signals. The average classification accuracies of using ACO reduced TDAR and WT features were 95.45+/-2.2% and 96.08+/-3.3%, respectively. The principal component analysis (PCA) was also conducted on the same data sets for comparison. The average classification accuracies of using PCA reduced TDAR and WT features were 91.51+/-4.9% and 89.87+/-4.4%, respectively. The results demonstrated that the proposed ACO-mRMR based feature selection method can achieve considerably high classification rates in sEMG motion classification task and be applicable to other biomedical signals pattern analysis.