Practical Handbook of Genetic Algorithms
Practical Handbook of Genetic Algorithms
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Feature selection based on a modified fuzzy C-means algorithm with supervision
Information Sciences—Informatics and Computer Science: An International Journal
Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
A multidimensional hybrid intelligent method for gear fault diagnosis
Expert Systems with Applications: An International Journal
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
A diversity-driven structure learning algorithm for building hierarchical neuro-fuzzy classifiers
Information Sciences: an International Journal
A rule-based intelligent method for fault diagnosis of rotating machinery
Knowledge-Based Systems
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Recent researches in fault classification have shown the importance of accurately selecting the features that have to be used as inputs to the diagnostic model. In this work, a multi-objective genetic algorithm (MOGA) is considered for the feature selection phase. Then, two different techniques for using the selected features to develop the fault classification model are compared: a single classifier based on the feature subset with the best classification performance and an ensemble of classifiers working on different feature subsets. The motivation for developing ensembles of classifiers is that they can achieve higher accuracies than single classifiers. An important issue for an ensemble to be effective is the diversity in the predictions of the base classifiers which constitute it, i.e. their capability of erring on different sub-regions of the pattern space. In order to show the benefits of having diverse base classifiers in the ensemble, two different ensembles have been developed: in the first, the base classifiers are constructed on feature subsets found by MOGAs aimed at maximizing the fault classification performance and at minimizing the number of features of the subsets; in the second, diversity among classifiers is added to the MOGA search as the third objective function to maximize. In both cases, a voting technique is used to effectively combine the predictions of the base classifiers to construct the ensemble output. For verification, some numerical experiments are conducted on a case of multiple-fault classification in rotating machinery and the results achieved by the two ensembles are compared with those obtained by a single optimal classifier.