Improvement of subgroup descriptions in noisy data by detecting exceptions

Author:

González PedroORCID,García-Vico Ángel Miguel,Carmona Cristóbal José,del Jesus María José

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference40 articles.

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2. Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M., Ventura, S., Garrell, J., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fernández, J., Herrera, F.: KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft. Comput. 13(3), 307–318 (2009)

3. Atzmueller, M., Puppe, F., Buscher, H.P.: Towards knowledge-intensive subgroup discovery. In: Proceedings of the Lernen–Wissensentdeckung-Adaptivität-Fachgruppe Maschinelles Lernen, pp. 111–117 (2004)

4. Brodley, C., Friedl, M.: Identifying mislabeled training data. J. Artif. Intell. 11, 131–167 (1999)

5. Carmona, C.J., Chrysostomou, C., Seker, H., del Jesus, M.J.: Fuzzy rules for describing subgroups from influenza a virus using a multi-objective evolutionary algorithm. Appl. Soft Comput. 13(8), 3439–3448 (2013)

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