Abstract
This work is related to the KEEL (Knowledge Extraction based on Evolutionary Learning) tool, a non-commercial software that supports data management, design of experiments and an educational section. The KEEL software tool is devoted to assess evolutionary algorithms for Data Mining problems including regression, classification, clustering, pattern mining and so on. These features implies an advantage for the research and educational field.
The aim of this contribution is to present some guidelines for including new algorithms in KEEL, helping the researchers to make their methods easily accessible for other authors and to compare the results of many approaches already included within the KEEL software. By providing a source code template, the developer does not need to take into account the basic requirements of the KEEL software tool, and he or she has only to focus in the designing and encoding of his or hers approach.
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References
Han, J., Kamber, M.: Data mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco (2006)
Romero, C., González, P., Ventura, S., del Jesus, M., Herrera, F.: Evolutionary algorithms for subgroup discovery in e-learning: A practical application using moodle data. Expert Systems with Applications 36(2), 1632–1644 (2009)
Otero, J., Sánchez, L., Alcalá-Fdez, J.: Fuzzy-genetic optimization of the parameters of a low cost system for the optical measurement of several dimensions of vehicles. Soft Computing 12(8), 751–764 (2008)
Mucientes, M., Moreno, D., Bugarín, A., Barro, S.: Design of a fuzzy controller in mobile robotics using genetic algorithms. Applied Soft Computing 7(2), 540–546 (2007)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2005)
Abeel, T., de Peer, Y.V., Saeys, Y.: Java-ML: A machine learning library. Journal of Machine Learning Research 10, 931–934 (2009)
Sonnenburg, S., Braun, M.L., Ong, C.S., Bengio, S., Bottou, L., Holmes, G., LeCun, Y., Müller, K.R., Pereira, F., Rasmussen, C.E., Rätsch, G., Schölkopf, B., Smola, A., Vincent, P., Weston, J., Williamson, R.: The need for open source software in machine learning. Journal of Machine Learning Research 8, 2443–2466 (2007)
Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M., Ventura, S., Garrell, J., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fernández, J., Herrera, F.: KEEL: A software tool to assess evolutionary algorithms to data mining problems. Soft Computing 13(3), 307–318 (2009)
Chi, Z., Yan, H., Pham, T.: Fuzzy algorithms with applications to image processing and pattern recognition. World Scientific, Singapore (1996)
Ventura, S., Romero, C., Zafra, A., Delgado, J.A., Hervás, C.: JCLEC: a java framework for evolutionary computation. Soft Computing 12(4), 381–392 (2007)
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Fernández, A., Luengo, J., Derrac, J., Alcalá-Fdez, J., Herrera, F. (2009). Implementation and Integration of Algorithms into the KEEL Data-Mining Software Tool. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_68
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DOI: https://doi.org/10.1007/978-3-642-04394-9_68
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