Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Particle Swarms for Feature Extraction of Hyperspectral Data
IEICE - Transactions on Information and Systems
A novel ACO-GA hybrid algorithm for feature selection in protein function prediction
Expert Systems with Applications: An International Journal
Feature selection using rough-DPSO in anomaly intrusion detection
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Computers in Biology and Medicine
Dimensionality reduction for evolving RBF networks with particle swarms
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Particle swarm optimization for feature selection in speaker verification
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
SVM classifier based feature selection using GA, ACO and PSO for siRNA design
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Efficient ant colony optimization for image feature selection
Signal Processing
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Feature selection is widely used to reduce dimension and remove irrelevant features. In this paper, particle swarm optimization is employed to select feature subset for classification task and train RBF neural network simultaneously. One advantage is that both the number of features and neural network configuration are encoded into particles, and in each iteration of PSO there is no iterative neural network training sub-algorithm. Another is that the fitness function considers three factors: mean squared error between neural network outputs and desired outputs, the complexity of network and the number of features, which guarantees strong generalization ability of RBF network. Furthermore, our approach could select as small-sized feature subset as possible to satisfy high accuracy requirement with rational training time. Experimental results on four datasets show that this method is attractive.