Abstract
A new machine learning system, INNER, is presented in this paper. The system starts out from a collection of training examples; some of them are inflated generalizing their description so as to obtain a first draft of classification rules. An optimization stage, borrowed from our previous system, Fan, is then applied to return the final set of rules. The main goal of Inner, besides its high level of accuracy, is its ability for self-maintenance. To close the paper, we present a number of different experiments carried` out with INNER to illustrate how good the performance and stability of the system is.
The research reported here was supported in part under grants PB95-1039 and PB96-1457 of the Spanish Agency Dirección General de Estudios Superiores.
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Luaces, O., Alonso, J., de la Cal, E.A., Ranilla, J., Bahamonde, A. (1998). Machine learning usefulness relies on accuracy and self-maintenance. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_430
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DOI: https://doi.org/10.1007/3-540-64574-8_430
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