By Giorgio Giacinto (auth.), Petra Perner (eds.)
These are the complaints of the 10th occasion of the economic convention on facts Mining ICDM held in Berlin (www.data-mining-forum.de). For this version this system Committee acquired a hundred seventy five submissions. After the pe- overview technique, we permitted forty nine high quality papers for oral presentation which are incorporated during this booklet. the subjects variety from theoretical features of information mining to app- cations of information mining resembling on multimedia facts, in advertising and marketing, finance and telec- munication, in drugs and agriculture, and in strategy keep an eye on, and society. prolonged models of chosen papers will look within the foreign magazine Trans- tions on laptop studying and information Mining (www.ibai-publishing.org/journal/mldm). Ten papers have been chosen for poster shows and are released within the ICDM Poster continuing quantity by means of ibai-publishing (www.ibai-publishing.org). along with ICDM 4 workshops have been hung on detailed scorching applicati- orientated issues in facts mining: info Mining in advertising DMM, facts Mining in LifeScience DMLS, the Workshop on Case-Based Reasoning for Multimedia facts CBR-MD, and the Workshop on info Mining in Agriculture DMA. The Workshop on info Mining in Agriculture ran for the 1st time this 12 months. All workshop papers could be released within the workshop lawsuits via ibai-publishing (www.ibai-publishing.org). chosen papers of CBR-MD might be released in a different factor of the foreign magazine Transactions on Case-Based Reasoning (www.ibai-publishing.org/journal/cbr).
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Additional info for Advances in Data Mining. Applications and Theoretical Aspects: 10th Industrial Conference, ICDM 2010, Berlin, Germany, July 12-14, 2010. Proceedings
547–552. AAAI Press, Menlo Park (1991) 9. : Correlation-based feature selection for discrete and numeric class machine learning. In: Proceeding of the 17th International Conference on Machine Learning, pp. 359–366. Morgan Kaufmann, San Francisco (2000) 10. : Eﬃcient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004) 11. : Eﬃcient dimensionality reduction approaches for feature selection. In: International Conference on Computational Intelligence and Multimedia Applications, vol.
Combining feature subsets in feature selection. , Roli, F. ) MCS 2005. LNCS, vol. 3541, pp. 165–175. Springer, Heidelberg (2005) Bootstrap Feature Selection for Ensemble Classiﬁers 41 17. : Feature Selection for Ensembles. In: AAAI 1999: Proceedings of the 16th National Conference on Artiﬁcial Intelligence, pp. 379–384. American Association for Artiﬁcial Intelligence, Menlo Park (1999) 18. : Asymmetric bagging and feature selection for activities prediction of drug molecules. Journal of BMC Bioinformatics 9, 1471–2105 (2008) 19.
According 40 R. Duangsoithong and T. Windeatt to the average results, bootstrap feature selection for ensemble classiﬁers provides accuracy slightly higher than the traditional feature selection for ensemble classiﬁers. The only drawback of this algorithm is the complexity which is increased due to selection of optimal features of each bootstrap replicate. Future work will investigate the result of bootstrap causal feature selection for ensemble classiﬁers. References 1. : Adaptive Control Processes: A Guided Tour.
Advances in Data Mining. Applications and Theoretical Aspects: 10th Industrial Conference, ICDM 2010, Berlin, Germany, July 12-14, 2010. Proceedings by Giorgio Giacinto (auth.), Petra Perner (eds.)