Abstract
The machine learning community has traditionally used the correct classification rate or accuracy to measure the performance of a classifier, generally avoiding the presentation of the sensitivities (i.e. the classification level of each class) in the results, especially in problems with more than two classes. Evolutionary Algorithms (EAs) are powerful global optimization techniques but they are very poor in terms of convergence performance. Consequently, they are frequently combined with Local Search (LS) algorithms that can converge in a few iterations. This paper proposes a new method for hybridizing EAs and LS techniques in a classification context, based on a clustering method that applies the k-means algorithm in the sensitivity space, obtaining groups of individuals that perform similarly for the different classes. Then, a representative of each group is selected and it is improved by a LS procedure. This method is applied in specific stages of the evolutionary process and we consider the minimun sensitivity and the accuracy as the evaluation measures. The proposed method is found to obtain classifiers with a better accuracy for each class than the application of the LS over the best individual of the population.
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Fernández-Navarro, F., Gutiérrez, P.A., Hervás-Martínez, C., Fernández, J.C. (2009). A Sensitivity Clustering Method for Hybrid Evolutionary Algorithms. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy. IWINAC 2009. Lecture Notes in Computer Science, vol 5601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02264-7_26
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DOI: https://doi.org/10.1007/978-3-642-02264-7_26
Publisher Name: Springer, Berlin, Heidelberg
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