Abstract
Feature or attribute selection is a crucial activity when knowledge discovery is applied to very large databases. Its main objective is to eliminate irrelevant or redundant attributes to obtain a computationally tractable problem, without affecting the classification quality. In this article a novel optimization approach is evaluated. This method uses concave programming to minimize the number of attributes to input to the mining algorithm and also, to minimize the classification error. This technique is evaluated using a billing data base from the national electric utility in Mexico. The results are compared against those obtained by traditional techniques. From this experimentation, several improvements to the optimization approach are suggested.
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© 2005 Springer-Verlag Berlin Heidelberg
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Mejía-Lavalle, M., Rodríguez, G., Arroyo, G. (2005). An Optimization Approach for Feature Selection in an Electric Billing Database. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_9
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DOI: https://doi.org/10.1007/11554028_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28897-8
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