Mikel Galar

TL;DR: A taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based is proposed and a thorough empirical comparison is developed by the consideration of the most significant published approaches to show whether any of them makes a difference.

TL;DR: This work develops a double study, using different base classifiers in order to observe the suitability and potential of each combination within each classifier, and compares the performance of these ensemble techniques with the classifiers' themselves.

TL;DR: Data Science can be considered as a discipline for discovering new and significant relationships, patterns and trends in the examination of large amounts of data in the search for knowledge contained in the information stored in large databases.

TL;DR: This paper develops a new ensemble construction algorithm (EUSBoost) based on RUSBoost, one of the simplest and most accurate ensemble, which combines random undersampling with Boosting algorithm, and proves that EUSBoost is able to outperform the state-of-the-art methods based on ensembles.

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.