Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Annalisa Appice,Pedro Pereira Rodrigues,Alípio Jor Conference p

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When is Undersampling Effective in Unbalanced Classification Tasks? learning of the classifier. Though this seems to work for the majority of cases, no detailed analysis exists about the impact of undersampling on the accuracy of the final classifier. This paper aims to fill this gap by proposing an integrated analysis of the two elements which have the largest imp

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A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisonsween the data items. The additional constraints are meant to reflect side-information that is not expressed in the feature vectors, directly. Relative comparisons can express structures at finer level of detail than must-link (ML) and cannot-link (CL) constraints that are commonly used for semi-supe

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978-3-319-23527-1Springer International Publishing Switzerland 2015

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Machine Learning and Knowledge Discovery in Databases978-3-319-23528-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Annalisa Appice,Pedro Pereira Rodrigues,Alípio JorIncludes supplementary material:

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620513.jpg

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https://doi.org/10.1007/978-3-319-23528-8data mining; foundations of machine learning and data mining; knowledge discovery in databases; probabi