In *Proceedings - 15th International Conference on Computing, CIC 2006*, 2006.

abstract bibtex

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.

@inProceedings{ title = {Instance selection and feature weighting using evolutionary algorithms}, type = {inProceedings}, year = {2006}, identifiers = {[object Object]}, id = {25893cd1-c21b-3092-9e1d-ec2ce79b2988}, created = {2017-12-10T20:09:59.058Z}, file_attached = {false}, profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367}, last_modified = {2018-03-09T18:07:13.561Z}, read = {false}, starred = {false}, authored = {true}, confirmed = {false}, hidden = {false}, private_publication = {false}, 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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