Abstract
We present a new algorithm called Ordered Classification, that is useful for classification problems where only few labeled examples are available but a large test set needs to be classified. In many real-world classification problems, it is expensive and some times unfeasible to acquire a large training set, thus, traditional supervised learning algorithms often perform poorly. In our algorithm, classification is performed by a discriminant approach similar to that of Query By Committee within the active learning setting. The method was applied to the real-world astronomical task of automated prediction of stellar atmospheric parameters, as well as to some benchmark learning problems showing a considerable improvement in classification accuracy over conventional algorithms.
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Solorio, T., Fuentes, O. (2002). Improving Classification Accuracy of Large Test Sets Using the Ordered Classification Algorithm. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_8
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DOI: https://doi.org/10.1007/3-540-36131-6_8
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