InstanceRank based on borders for instance selection
Por:
Hernandez-Leal P., Carrasco-Ochoa J.A., Martínez-Trinidad J.F., Olvera-Lopez J.A.
Publicada:
1 ene 2013
Resumen:
Instance selection algorithms are used for reducing the number of training instances. However, most of them suffer from long runtimes which results in the incapability to be used with large datasets. In this work, we introduce an Instance Ranking per class using Borders (instances near to instances belonging to different classes), using this ranking we propose an instance selection algorithm (IRB). We evaluated the proposed algorithm using k-NN with small and large datasets, comparing it against state of the art instance selection algorithms. In our experiments, for large datasets IRB has the best compromise between time and accuracy. We also tested our algorithm using SVM, LWLR and C4.5 classifiers, in all cases the selection computed by our algorithm obtained the best accuracies in average. © 2012 Elsevier Ltd All rights reserved.
Filiaciones:
Hernandez-Leal P.:
National Institute of Astrophysics, Optics and Electronics, Computer Science Department, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP 72840, Mexico
Carrasco-Ochoa J.A.:
National Institute of Astrophysics, Optics and Electronics, Computer Science Department, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP 72840, Mexico
Martínez-Trinidad J.F.:
National Institute of Astrophysics, Optics and Electronics, Computer Science Department, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP 72840, Mexico
Olvera-Lopez J.A.:
Benemérita Universidad Autónoma de Puebla, Computer Science Department, Ciudad Universitaria, Av. San Claudio y 14 Sur, Puebla, CP 72570, Mexico
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