Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Machine Learning
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
From complex environments to complex behaviors
Adaptive Behavior - Special issue on environment structure and behavior
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
The handbook of brain theory and neural networks
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Visualization and interactive feature selection for unsupervised data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Feature Selection in Conceptual Clustering
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Subset Selection and Order Identification for Unsupervised Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Efficient and Scalable Pareto Optimization by Evolutionary Local Selection Algorithms
Evolutionary Computation
New approach for extracting knowledge from the XCS learning classifier system
International Journal of Hybrid Intelligent Systems
Mining Multidimensional Data through Element Oriented Analysis
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Finding Clothing That Fit through Cluster Analysis and Objective Interestingness Measures
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Data mining and preprocessing application on component reports of an airline company in Turkey
Expert Systems with Applications: An International Journal
Hybrid feature selection method for supervised classification based on Laplacian score ranking
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Feature evaluation and selection with cooperative game theory
Pattern Recognition
A novel multi-stage feature selection method for microarray expression data analysis
International Journal of Data Mining and Bioinformatics
Word level feature discovery to enhance quality of assertion mining
Proceedings of the International Conference on Computer-Aided Design
RFS: Efficient feature selection method based on R-value
Computers in Biology and Medicine
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Feature subset selection is an important problem in knowledge discovery, not only for the insight gained from determining relevant modeling variables, but also for the improved understandability, scalability, and, possibly, accuracy of the resulting models. The purpose of this chapter is to provide a comprehensive analysis of feature selection via evolutionary search in supervised and unsupervised learning. To achieve this purpose, we first discuss a general framework for feature selection based on a new search algorithm, Evolutionary Local Selection Algorithm (ELSA). The search is formulated as a multi-objective optimization problem to examine the trade-off between the complexity of the generated solutions against their quality. ELSA considers multiple objectives efficiently while avoiding computationally expensive global comparison. We combine ELSA with Artificial Neural Networks (ANNs) and Expectation-Maximization (EM) algorithms for feature selection in supervised and unsupervised learning respectively. Further, we provide a new two-level evolutionary algorithm, Meta-Evolutionary Ensembles (MEE), where feature selection is used to promote the diversity among classifiers in the same ensemble.