C4.5: programs for machine learning
C4.5: programs for machine learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Combinatorial feature selection problems
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Consistency measures for feature selection
Journal of Intelligent Information Systems
Expert Systems with Applications: An International Journal
Online Objective Reduction to Deal with Many-Objective Problems
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Pareto front feature selection: using genetic programming to explore feature space
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
A decision rule-based method for feature selection in predictive data mining
Expert Systems with Applications: An International Journal
Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
Feature selection in heterogeneous structure of ensembles: a genetic algorithm approach
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A comparison of machine learning techniques for detection of drug target articles
Journal of Biomedical Informatics
Use of Multiobjective Genetic Algorithms in Feature Selection
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
A fractal dimension based filter algorithm to select features for supervised learning
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Correlation-based and causal feature selection analysis for ensemble classifiers
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
Evolutionary Rough Feature Selection in Gene Expression Data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Generation of neural networks using a genetic algorithm approach
International Journal of Bio-Inspired Computation
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Feature Selection may be viewed as a search for optimal feature subsets considering one or more importance criteria. This search may be performed with Multi-objective Genetic Algorithms. In this work, we present an application of these algorithms for combining different filter approach criteria, which rely on general characteristics of the data, as feature-class correlation, to perform the search for subsets of features. We conducted experiments on public data sets and the results show the potential of this proposal when compared to mono-objective genetic algorithms and two popular filter algorithms.