Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Memetic algorithms: a short introduction
New ideas in optimization
A model selection approach for local learning
AI Communications - Special issue on AI research in the Benelux
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature Selection with Selective Sampling
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision tree-based attribute weighting filter for naive Bayes
Knowledge-Based Systems
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
Markov blanket-embedded genetic algorithm for gene selection
Pattern Recognition
A genetic algorithm-based method for feature subset selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Niching Memetic Algorithm for Simultaneous Clustering and Feature Selection
IEEE Transactions on Knowledge and Data Engineering
Memetic Algorithms for Feature Selection on Microarray Data
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A Hybrid Genetic Algorithm for Simultaneous Feature Selection and Rule Learning
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
An optimization of ReliefF for classification in large datasets
Data & Knowledge Engineering
Gene selection for classifying microarray data using grey relation analysis
DS'06 Proceedings of the 9th international conference on Discovery Science
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking
Knowledge-Based Systems
A two-grade approach to ranking interval data
Knowledge-Based Systems
A fuzzy intelligent approach to the classification problem in gene expression data analysis
Knowledge-Based Systems
A novel business cycle surveillance system using the query logs of search engines
Knowledge-Based Systems
Large-margin feature selection for monotonic classification
Knowledge-Based Systems
Face recognition using discriminant sparsity neighborhood preserving embedding
Knowledge-Based Systems
A Bayesian stochastic search method for discovering Markov boundaries
Knowledge-Based Systems
Feature selection using dynamic weights for classification
Knowledge-Based Systems
Hybridising harmony search with a Markov blanket for gene selection problems
Information Sciences: an International Journal
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A novel correlation based memetic framework (MA-C) which is a combination of genetic algorithm (GA) and local search (LS) using correlation based filter ranking is proposed in this paper. The local filter method used here fine-tunes the population of GA solutions by adding or deleting features based on Symmetrical Uncertainty (SU) measure. The focus here is on filter methods that are able to assess the goodness or ranking of the individual features. Empirical study of MA-C on several commonly used datasets from the large-scale Gene expression datasets indicates that it outperforms recent existing methods in the literature in terms of classification accuracy, selected feature size and efficiency. Further, we also investigate the balance between local and genetic search to maximize the search quality and efficiency of MA-C.