Instance selection and feature weighting using evolutionary algorithms. Ramírez-Cruz, J.; Fuentes, O.; Alarcón-Aquino, V.; and García-Banuelos, L. In Proceedings - 15th International Conference on Computing, CIC 2006, 2006.
abstract   bibtex   
Machine learning algorithms are commonly used in real-world applications for solving complex problems where it is difficult to get a mathematical model. The goal of machine learning algorithms is to learn an objective function from a set of training examples where each example is defined by a feature set. Regularly, real word applications have many examples with many features; however, the objective function depends on few of them. The presence of noisy examples or irrelevant features in a dataset degrades the performance of machine learning algorithms; such is the case of k-nearest neighbor machine learning algorithm (k-NN). Thus choosing good instance and feature subsets may improve the algorithm's performance. Evolutionary algorithms proved to be good techniques for finding solutions in a large solution space and to be stable in the presence of noise. In this work, we address the problem of instance selection and feature weighting for instance-based methods by means of a Genetic Algorithm (GA) and Evolution strategies (ES). We show that combining GA and ES with a k-NN algorithm can improve the predictive accuracy of the resulting classifier. © 2006 IEEE.
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 title = {Instance selection and feature weighting using evolutionary algorithms},
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 abstract = {Machine learning algorithms are commonly used in real-world applications for solving complex problems where it is difficult to get a mathematical model. The goal of machine learning algorithms is to learn an objective function from a set of training examples where each example is defined by a feature set. Regularly, real word applications have many examples with many features; however, the objective function depends on few of them. The presence of noisy examples or irrelevant features in a dataset degrades the performance of machine learning algorithms; such is the case of k-nearest neighbor machine learning algorithm (k-NN). Thus choosing good instance and feature subsets may improve the algorithm's performance. Evolutionary algorithms proved to be good techniques for finding solutions in a large solution space and to be stable in the presence of noise. In this work, we address the problem of instance selection and feature weighting for instance-based methods by means of a Genetic Algorithm (GA) and Evolution strategies (ES). We show that combining GA and ES with a k-NN algorithm can improve the predictive accuracy of the resulting classifier. © 2006 IEEE.},
 bibtype = {inProceedings},
 author = {Ramírez-Cruz, J.-F. and Fuentes, O. and Alarcón-Aquino, V. and García-Banuelos, L.},
 booktitle = {Proceedings - 15th International Conference on Computing, CIC 2006}
}
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