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Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011; 01/2011
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Journal of Machine Learning Research - Proceedings Track. 01/2010; 8:122-135.
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Proceedings of the Ninth IEEE International Conference on Data Mining (ICDM 2009); 01/2009
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Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008 Proceedings; 01/2008
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ABSTRACT: In this study, we evaluated the impact of long-term occupational exposure to elemental mercury vapor (Hg0) on the personality traits of ex-mercury miners. Study groups included 53 ex-miners previously exposed to Hg0 and 53 age-matched controls. Miners and controls completed the self-reporting Eysenck Personality Questionnaire and the Emotional States Questionnaire. The relationship between the indices of past occupational exposure and the observed personality traits was evaluated using Pearson's correlation coefficient and on a subgroup level by machine learning methods (regression trees). The ex-mercury miners were intermittently exposed to Hg0 for a period of 7-31 years. The means of exposure-cycle urine mercury (U-Hg) concentrations ranged from 20 to 120 microg/L. The results obtained indicate that ex-miners tend to be more introverted and sincere, more depressive, more rigid in expressing their emotions and are likely to have more negative self-concepts than controls, but no correlations were found with the indices of past occupational exposure. Despite certain limitations, results obtained by the regression tree suggest that higher alcohol consumption per se and long-term intermittent, moderate exposure to Hg0 (exposure cycle mean U-Hg concentrations > 38.7 < 53.5 microg/L) in interaction with alcohol remain a plausible explanation for the depression associated with negative self-concept found in subgroups of ex-mercury miners. This could be one of the reason for the higher risk of suicide among miners of the Idrija Mercury Mine in the last 45 years.
Environmental Health Perspectives 02/2006; 114(2):290-6. · 7.04 Impact Factor
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Knowledge Discovery in Inductive Databases, 4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers; 01/2005
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Machine Learning. 01/2004; 54:255-273.
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SIGKDD Explorations. 01/2004; 6:155-156.
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Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), University of New South Wales, Sydney, Australia, July 8-12, 2002; 01/2002
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Multiple Classifier Systems, Third International Workshop, MCS 2002, Cagliari, Italy, June 24-26, 2002, Proceedings; 01/2002
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Machine Learning: ECML 2002, 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002, Proceedings; 01/2002
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ABSTRACT: In this paper, we present an integration of the algorithm MLC4.5 for learning meta decision trees (MDTs) into the Weka data mining suite. MDTs are a method for combining multiple classifiers. Instead of giving a prediction, MDT leaves specify which classifier should be used to obtain a prediction. The algorithm is based on the C4.5 algorithm for learning ordinary decision trees. An extensive performance evaluation of stacking with MDTs on twenty-one data sets has been performed. We combine base-level classifiers generated by three learning algorithms: an algorithm for learning decision trees, a nearest neighbor algorithm and a naive Bayes algorithm. We compare MDTs to bagged and boosted decision trees, and to combined classifiers with voting and three di#erent stacking methods: with ordinary decision trees, with naive Bayes algorithm and with multi-response linear regression as a meta-level classifier. In terms of performance, stacking with MDTs gives better results than other methods except when compared to stacking with multi-response linear regression as a meta-level classifier; the latter is slightly better than MDTs. 1
10/2001;
