Floating search methods in feature selection
Pattern Recognition Letters
Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization of Intrusion Detection through Fast Hybrid Feature Selection
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Improving genetic algorithms' efficiency using intelligent fitness functions
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
A Wrapper for Feature Selection Based on Mutual Information
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Neural modeling of relative air humidity
Computers and Electronics in Agriculture
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Training support vector machines using greedy stagewise algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A hybrid feature selection approach for microarray gene expression data
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A general regression neural network
IEEE Transactions on Neural Networks
Correntropy based feature selection using binary projection
Pattern Recognition
A hybrid approach to feature subset selection for brain-computer interface design
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
TC-VGC: A Tumor Classification System using Variations in Genes' Correlation
Computer Methods and Programs in Biomedicine
Gene selection and PSO-BP classifier encoding a prior information
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
User-oriented ontology-based clustering of stored memories
Expert Systems with Applications: An International Journal
Selecting feature subset via constraint association rules
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Similarity measurement and feature selection using genetic algorithm
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Computer Vision and Image Understanding
Applied Soft Computing
Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis
Computer Methods and Programs in Biomedicine
A random forest classifier for lymph diseases
Computer Methods and Programs in Biomedicine
A novel feature subset selection algorithm based on association rule mining
Intelligent Data Analysis
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Searching for an optimal feature subset from a high dimensional feature space is known to be an NP-complete problem. We present a hybrid algorithm, SAGA, for this task. SAGA combines the ability to avoid being trapped in a local minimum of simulated annealing with the very high rate of convergence of the crossover operator of genetic algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks. We compare the performance over time of SAGA and well-known algorithms on synthetic and real datasets. The results show that SAGA outperforms existing algorithms.