KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)

KEEL-dataset - Experimental studies with Classification data sets

Introduction
Experimental studies related

This section shows some relevant research papers in which some of the classification data sets avalaible in KEEL-dataset have been employed.

For each study, we provide its reference (plain text and BibTeX formats), abstract and summary. A pdf version the article can also be downloaded. Additionally, we offer complementary material about the experimental studies carried up: Algorithms tested, data sets employed and results obtained (XLS and CVS formats).

Experimental studies and results with these data sets

Jump to year: 2010 (2)

Year 2010 (2):

dataset/studies/classification/fernandez-IEEETEC10.gif

A. Fernandez, S. García, J. Luengo, E. Bernadó-Mansilla, F. Herrera, Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy and Comparative Study. IEEE Transactions on Evolutionary Computation, Doi: 10.1109/TEVC.2009.2039140, In press (2010).

Link to: Data sets, algorithms and experimental results.

Link to: Website associated to this paper.

dataset/studies/classification/derrac-PR10.gif

J. Derrac, S. García, F. Herrera, IFS-CoCo: Instance and Feature Selection based on Cooperative Coevolution with Nearest Neighbor Rule. Pattern Recognition 43:6 (2010) 2082-2105. doi: 10.1016/j.patcog.2009.12.012 .

Link to: Data sets, algorithms and experimental results.