Ander Ansuategi

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Accurate prediction is probably the most pursued objective when solving real problems with machine learning, but there are situations where, added to the prediction, it is important to obtain a comprehensible output. The aim of this work is to compare the behaviour of two strategies to combine the knowledge of m classifiers in a single one in order to(More)
Being aware of the importance of classifiers to be comprehensible when using machine learning to solve real world problems, bagging needs a way to be explained. This work compares Consolidated Tree’s Construction (CTC) algorithm with the Combined Multiple Models (CMM) method proposed by Domingos when used to extract explanation of the classification made by(More)
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