IKAT, Universiteit Maastricht. "-//W3C//DTD HTML 4.01 Transitional//EN\">, Heart Disease Data Set Machine Learning, 38. Centre for Informatics and Applied Optimization, School of Information Technology and Mathematical Sciences, University of Ballarat. ICDM. [View Context].Peter L. Hammer and Alexander Kogan and Bruno Simeone and Sandor Szedm'ak. (perhaps "call") 56 cday: day of cardiac cath (sp?) [View Context].Pedro Domingos. #9 (cp) 4. [View Context].Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. Efficient Mining of High Confidience Association Rules without Support Thresholds. J. Artif. IEEE Trans. 2. Intell, 12. [Web Link] Gennari, J.H., Langley, P, & Fisher, D. (1989). [View Context].John G. Cleary and Leonard E. Trigg. Budapest: Andras Janosi, M.D. Boosted Dyadic Kernel Discriminants. [View Context].Bruce H. Edmonds. Budapest: Andras Janosi, M.D. Pattern Anal. Geometry in Learning. [View Context].Kristin P. Bennett and Ayhan Demiriz and John Shawe-Taylor. Unanimous Voting using Support Vector Machines. These are more common in domains with human data such as healthcare and education. #16 (fbs) 7. A new nonsmooth optimization algorithm for clustering. [View Context].D. [Web Link] David W. Aha & Dennis Kibler. [View Context].Igor Kononenko and Edvard Simec and Marko Robnik-Sikonja. NeC4.5: Neural Ensemble Based C4.5. The "goal" field refers to the presence of heart disease in the patient. IEEE Trans. [View Context].Zhi-Hua Zhou and Yuan Jiang. Dissertation Towards Understanding Stacking Studies of a General Ensemble Learning Scheme ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Naturwissenschaften. International application of a new probability algorithm for the diagnosis of coronary artery disease. Centre for Policy Modelling. On predictive distributions and Bayesian networks. [View Context].Rudy Setiono and Wee Kheng Leow. Rev, 11. An Implementation of Logical Analysis of Data. [View Context].Jeroen Eggermont and Joost N. Kok and Walter A. Kosters. Department of Decision Sciences and Engineering Systems & Department of Mathematical Sciences, Rensselaer Polytechnic Institute. KDD. A Comparative Analysis of Methods for Pruning Decision Trees. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. The University of Birmingham. Department of Computer Science University of Massachusetts. [View Context].Rudy Setiono and Wee Kheng Leow. In Fisher. Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. [View Context].Adil M. Bagirov and John Yearwood. For more information about networks and the terms used to describe the datasets, click Getting Started. We show that a seemingly unrelated missing data problem (imputing missing values and learning subsequent tasks) can naturally be solved with graphs and we propose the first graph-based solution to solve the problem. V.A. UCI Spambase Dataset. 2003. To see Test Costs (donated by Peter Turney), please see the folder "Costs", Only 14 attributes used: 1. Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm. Using Localised `Gossip' to Structure Distributed Learning. [View Context].Peter D. Turney. Machine Learning, 24. Stanford University. Unsupervised and supervised data classification via nonsmooth and global optimization. Issues in Stacked Generalization. [View Context].Ron Kohavi and Dan Sommerfield. Randall Wilson and Roel Martinez. Network architecture of the Internet, telephone networks, cable networks, and cell phone networks. Data Set Information: This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. Diversity in Neural Network Ensembles. #3 (age) 2. School of Computing National University of Singapore. Network performance models. 1999. Kaizhu Huang and Haiqin Yang and Irwin King and Michael R. Lyu and Laiwan Chan. Generating rules from trained network using fast pruning. A major problem that every email and messaging service is continuously working on is to classify emails as spam or non-spam. 1999. Advogato dataset of a trust network in a online web community, Collections of classic network datasets commonly used in social network analysis research, Dataset directories curated by research groups and organizations, Dataset directories curated by individuals. Biased Minimax Probability Machine for Medical Diagnosis. 2000. [Web Link]. #40 (oldpeak) 11. -T Lin and C. -J Lin. ejection fraction 50 exerwm: exercise wall (sp?) The typicalness framework: a comparison with the Bayesian approach. CEFET-PR, Curitiba. #38 (exang) 10. [View Context].Ron Kohavi and George H. John. The UCI Spambase dataset contains 4601 emails and 57 meta-information about the emails. All four unprocessed files also exist in this directory. Proceedings of the International Joint Conference on Neural Networks. Machine Learning: Proceedings of the Fourteenth International Conference, Morgan. [View Context].Zhi-Hua Zhou and Xu-Ying Liu. 2001. Dept. [View Context].Endre Boros and Peter Hammer and Toshihide Ibaraki and Alexander Kogan and Eddy Mayoraz and Ilya B. Muchnik. A Second order Cone Programming Formulation for Classifying Missing Data. 2000. [View Context].Wl odzisl and Rafal Adamczak and Krzysztof Grabczewski and Grzegorz Zal. The names and social security numbers of the patients were recently removed from the database, replaced with dummy values. 1997. GRAPE is a general framework for feature imputation and label prediction in the presence of missing data. 1997. Experiences with OB1, An Optimal Bayes Decision Tree Learner. [View Context].H. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. A Column Generation Algorithm For Boosting. 3. [View Context]. Lincoln Labs set up an environment to acquire nine weeks of raw TCP dump data for a local-area network (LAN) simulating a typical U.S. Air Force LAN. Using Rules to Analyse Bio-medical Data: A Comparison between C4.5 and PCL. [View Context].Rudy Setiono and Huan Liu. Improved Generalization Through Explicit Optimization of Margins. [View Context].Wl/odzisl/aw Duch and Karol Grudzinski and Geerd H. F Diercksen. WAIM. Lots of years. Genetic Programming for data classification: partitioning the search space. [View Context].Jinyan Li and Limsoon Wong. #12 (chol) 6. 2002. [View Context].Baback Moghaddam and Gregory Shakhnarovich. A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods. INDEPENDENT VARIABLE GROUP ANALYSIS IN LEARNING COMPACT REPRESENTATIONS FOR DATA. [View Context].Gavin Brown. Hungarian Institute of Cardiology. 8 = bike 125 kpa min/min 9 = bike 100 kpa min/min 10 = bike 75 kpa min/min 11 = bike 50 kpa min/min 12 = arm ergometer 29 thaldur: duration of exercise test in minutes 30 thaltime: time when ST measure depression was noted 31 met: mets achieved 32 thalach: maximum heart rate achieved 33 thalrest: resting heart rate 34 tpeakbps: peak exercise blood pressure (first of 2 parts) 35 tpeakbpd: peak exercise blood pressure (second of 2 parts) 36 dummy 37 trestbpd: resting blood pressure 38 exang: exercise induced angina (1 = yes; 0 = no) 39 xhypo: (1 = yes; 0 = no) 40 oldpeak = ST depression induced by exercise relative to rest 41 slope: the slope of the peak exercise ST segment -- Value 1: upsloping -- Value 2: flat -- Value 3: downsloping 42 rldv5: height at rest 43 rldv5e: height at peak exercise 44 ca: number of major vessels (0-3) colored by flourosopy 45 restckm: irrelevant 46 exerckm: irrelevant 47 restef: rest raidonuclid (sp?)
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