Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Consistency-based search in feature selection
Artificial Intelligence
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
LS Bound based gene selection for DNA microarray data
Bioinformatics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The coefficient of intrinsic dependence (feature selection using el CID)
Pattern Recognition
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Memetic Algorithms for Feature Selection on Microarray Data
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Gene boosting for cancer classification based on gene expression profiles
Pattern Recognition
A memetic algorithm for gene selection and molecular classification of cancer
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IEEE Transactions on Information Technology in Biomedicine
Expert Systems with Applications: An International Journal
Ensemble gene selection by grouping for microarray data classification
Journal of Biomedical Informatics
Ensemble gene selection for cancer classification
Pattern Recognition
Identification of Full and Partial Class Relevant Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A wrapper-based feature selection method for ADMET prediction using evolutionary computing
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Towards a memetic feature selection paradigm
IEEE Computational Intelligence Magazine
Echo state networks with sparse output connections
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
On the effectiveness of gene selection for microarray classification methods
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Feature selection for MAUC-oriented classification systems
Neurocomputing
DBNs-BLR (MCMC) -GAs-KNN: a novel framework of hybrid system for thalassemia expert system
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
Gene selection with guided regularized random forest
Pattern Recognition
Hybridising harmony search with a Markov blanket for gene selection problems
Information Sciences: an International Journal
Diverse accurate feature selection for microarray cancer diagnosis
Intelligent Data Analysis
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Microarray technologies enable quantitative simultaneous monitoring of expression levels for thousands of genes under various experimental conditions. This new technology has provided a new way of biological classification on a genome-wide scale. However, predictive accuracy is affected by the presence of thousands of genes many of which are unnecessary from the classification point of view. So, a key issue of microarray data classification is to identify the smallest possible set of genes that can achieve good predictive accuracy. In this study, we propose a novel Markov blanket-embedded genetic algorithm (MBEGA) for gene selection problem. In particular, the embedded Markov blanket-based memetic operators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improve the solution and fine-tune the search. Empirical results on synthetic and microarray benchmark datasets suggest that MBEGA is effective and efficient in eliminating irrelevant and redundant features based on both Markov blanket and predictive power in classifier model. A detailed comparative study with other methods from each of filter, wrapper, and standard GA shows that MBEGA gives a best compromise among all four evaluation criteria, i.e., classification accuracy, number of selected genes, computational cost, and robustness.