Jesús Alcalá-Fdez

TL;DR: The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformat and some guidelines for including new algorithms in KEEL, helping the researcher to compare the results of many approaches already included within the KEEL software.

TL;DR: KEEL as discussed by the authors is a software tool to assess evolutionary algorithms for data mining problems of various kinds including regression, classification, unsupervised learning, etc., which includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL.

TL;DR: This method limits the order of the associations in the association rule extraction and considers the use of subgroup discovery, which is based on an improved weighted relative accuracy measure to preselect the most interesting rules before a genetic postprocessing process for rule selection and parameter tuning.

TL;DR: The most recent components added to KEEL 3.0 are described, including new modules for semi-supervised learning, multi-instance learning, imbalanced classification and subgroup discovery, which greatly improve the versatility of KEEL to deal with more modern data mining problems.

TL;DR: This procedure attempts to estimate the missing information in an expert's incomplete preference relation using only the preference values provided by that particular expert using the additive consistency property.