Instance Selection and Feature Weighting Using Evolutionary Algorithms. Ramirez-Cruz, J., Fuentes, O., Alarcon-Aquino, V., & Garcia-Banuelos, L. In 2006 15th International Conference on Computing, pages 73-79, 11, 2006. IEEE. ![Instance Selection and Feature Weighting Using Evolutionary Algorithms [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi 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 world 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.
@inproceedings{
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 world 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.},
bibtype = {inproceedings},
author = {Ramirez-Cruz, Jose-Federico and Fuentes, Olac and Alarcon-Aquino, Vicente and Garcia-Banuelos, Luciano},
doi = {10.1109/CIC.2006.42},
booktitle = {2006 15th International Conference on Computing}
}
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