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
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Digging into acceptor splice site prediction: an iterative feature selection approach
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Feature subset selection bias for classification learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Information preserving multi-objective feature selection for unsupervised learning
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Unsupervised and supervised machine learning in user modeling for intelligent learning environments
Proceedings of the 12th international conference on Intelligent user interfaces
CT-EXT: an algorithm for computing typical testor set
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Typical testors generation based on an evolutionary algorithm
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Identification of risk factors for TRALI using a hybrid algorithm
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Evolutionary computation in the identification of risk factors. Case of TRALI
Expert Systems with Applications: An International Journal
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Feature subset selection for unsupervised learning, is a very important topic in artificial intelligence because it is the base for saving computational resources. In this implementation we use a typical testor's methodology in order to incorporate an importance index for each variable. This paper presents the general framework and the way two hybridized meta-heuristics work in this NP-complete problem. The evolutionary mechanisms are based on the Univariate Marginal Distribution Algorithm (UMDA) and the Genetic Algorithm (GA). GA and UMDA --- Estimation of Distribution Algorithm (EDA) use a very useful rapid operator implemented for finding typical testors on a very large dataset and also, both algorithms, have a local search mechanism for improving time and fitness. Experiments show that EDA is faster than GA because it has a better exploitation performance; nevertheless, GA' solutions are more consistent.