Victoria López

TL;DR: This work carries out a thorough discussion on the main issues related to using data intrinsic characteristics in this classification problem, and introduces several approaches and recommendations to address these problems in conjunction with imbalanced data.

TL;DR: A survey of discretization methods can be found in this paper, where the main goal is to transform a set of continuous attributes into discrete ones, by associating categorical values to intervals and thus transforming quantitative data into qualitative data.

TL;DR: This experimental study will include several well-known algorithms from the literature such as decision trees, support vector machines and instance-based learning, with the intention of obtaining global conclusions from different classification paradigms.

TL;DR: This work analyzes the performance of several techniques used to deal with imbalanced datasets in the big data scenario using the Random Forest classifier, and shows that there is not an approach to imbalanced big data classification that outperforms the others for all the data considered when using Random Forest.

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.