Strong Compound-Risk Factors: Efficient Discovery Through Emerging Patterns and Contrast Sets
暂无分享,去创建一个
[1] Jinyan Li,et al. Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.
[2] Pedro M. Domingos,et al. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.
[3] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[4] Steve Evans,et al. Step by step: Breaking outsourcing down into manageable phases , 2004 .
[5] Kotagiri Ramamohanarao,et al. The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms , 2000, ICML.
[6] J. Zhang,et al. What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. , 1998, JAMA.
[7] Jinyan Li,et al. Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns , 2002, Bioinform..
[8] Gerd Stumme,et al. Mining frequent patterns with counting inference , 2000, SKDD.
[9] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[10] J. Deeks. When can odds ratios mislead? , 1998 .
[11] Douglas G Altman,et al. Odds ratios should be avoided when events are common , 1998, BMJ.
[12] M. Bracken,et al. When can odds ratios mislead? Avoidable systematic error in estimating treatment effects must not be tolerated. , 1998, BMJ.
[13] Jianping Li,et al. On the complexity of finding emerging patterns , 2004, Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004..
[14] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[15] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[16] Huiqing Liu,et al. Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients , 2003, Bioinform..
[17] J. Terwilliger. Genetic Variation and Human Disease: Principles and Evolutionary Approaches , 1997 .
[18] Geoffrey I. Webb,et al. On detecting differences between groups , 2003, KDD '03.
[19] Bracken Mb,et al. When can odds ratios mislead? Avoidable systematic error in estimating treatment effects must not be tolerated. , 1998 .
[20] H. Davies,et al. When can odds ratios mislead? , 1998, BMJ.
[21] J. Manson,et al. Male pattern baldness and coronary heart disease: the Physicians' Health Study. , 2000, Archives of internal medicine.
[22] D. Neumark-Sztainer,et al. The social environments of adolescents: associations between socioenvironmental factors and health behaviors during adolescence. , 1999, Adolescent medicine.
[23] B van Hout,et al. How should different life expectancies be valued? Diminishing marginal utility and discounting future effects have similar consequences. , 1998, BMJ.
[24] Gerhard Tutz,et al. A CART-based approach to discover emerging patterns in microarray data , 2003, Bioinform..
[25] Jinyan Li,et al. Relative risk and odds ratio: a data mining perspective , 2005, PODS '05.
[26] J. Downing,et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.
[27] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[28] Sylvia Wassertheil-Smoller,et al. Biostatistics and Epidemiology , 1995, Springer New York.
[29] H Hausen,et al. Patients' expectations of an ideal dentist and their views concerning the dentist they visited: do the views conform to the expectations and what determines how well they conform? , 1996, Community dentistry and oral epidemiology.
[30] Stephen D. Bay,et al. Detecting Group Differences: Mining Contrast Sets , 2001, Data Mining and Knowledge Discovery.
[31] Pat Langley,et al. An Analysis of Bayesian Classifiers , 1992, AAAI.
[32] Ron Rymon,et al. Search through Systematic Set Enumeration , 1992, KR.
[33] G. Wright,et al. Patient Management: A review of patient satisfaction: 2. Dental patient satisfaction: an appraisal of recent literature , 1999, British Dental Journal.
[34] M. Espeland,et al. Satisfaction of the older patient with dental care. , 1986, Gerodontics.
[35] Stephen D. Bay,et al. Detecting change in categorical data: mining contrast sets , 1999, KDD '99.
[36] Kotagiri Ramamohanarao,et al. Fast discovery and the generalization of strong jumping emerging patterns for building compact and accurate classifiers , 2006, IEEE Transactions on Knowledge and Data Engineering.
[37] J. Hair. Multivariate data analysis , 1972 .
[38] Prh Newsome,et al. A review of patient satisfaction , 1999 .
[39] Jonathan J. Deeks,et al. Down with odds ratios! , 1996, Evidence Based Medicine.
[40] T. Cook. Advanced statistics: up with odds ratios! A case for odds ratios when outcomes are common. , 2002, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.
[41] James Bailey,et al. Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams , 2006, KDD '06.
[42] A. Hajeer. The Genetic Variation and Human Disease: Principles and Evolutionary Approaches , 1996 .
[43] Nicolas Pasquier,et al. Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.
[44] Jian Pei,et al. Minimum Description Length Principle: Generators Are Preferable to Closed Patterns , 2006, AAAI.