Jose G. Moreno-Torres

TL;DR: This work attempts to present a unifying framework through the review and comparison of some of the most important works in the literature on dataset shift, and uses different names to refer to the same concepts.

TL;DR: This work analyzes the performance of data level proposals against algorithm level proposals focusing in cost-sensitive models and versus a hybrid procedure that combines those two approaches to show that an unique approach among the rest cannot be highlighted.

TL;DR: From the experimental results obtained, it is concluded that the degree of partition-induced covariate shift depends on the cross-validation scheme considered, and worse schemes may harm the correctness of a single-classifier performance estimation and also increase the needed number of repetitions of cross- validation to reach a stable performance estimation.

TL;DR: A relative bidimensional index is proposed that takes into account both the net production and the quality of it, as an attempt to provide a comprehensive and objective way to compare the research output of different institutions in a specific field, using journal contributions and citations.

TL;DR: This paper presents a genetics-based machine learning approach that performs feature extraction on data from a lab to help increase the classification performance of an existing classifier that was built using theData from a different laboratory which uses the same protocols, while learning the shape of the fractures between data that motivated the bad behavior.