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Description

This database is a standardized version of the original audiology database (see audiology.* in this directory). The non-standard set of attributes have been converted to a standard set of attributes according to the rules that follow. * Each property that appears anywhere in the original .data or .test file has been represented as a separate attribute in this file. * A property such as age_gt_60 is represented as a boolean attribute with values f and t. * In most cases, a property of the form x(y) is represented as a discrete attribute x() whose possible values are the various y's; air() is an example. There are two exceptions: ** when only one value of y appears anywhere, e.g. static(normal). In this case, x_y appears as a boolean attribute. ** when one case can have two or more values of x, e.g. history(..). All possible values of history are treated as separate boolean attributes. * Since boolean attributes only appear as positive conditions, each boolean attribute is assumed to be false unless noted as true. The value of multi-value discrete attributes taken as unknown ("?") unless a value is specified. * The original case identifications, p1 to p200 in the .data file and t1 to t26 in the .test file, have been added as a unique identifier attribute. [Note: in the original .data file, p165 has a repeated specification of o_ar_c(normal); p166 has repeated specification of speech(normal) and conflicting values air(moderate) and air(mild). No other problems with the original data were noted.]

Related Papers

• Thomas G. Dietterich and Ghulum Bakiri. Solving Multiclass Learning Problems via Error-Correcting Output Codes. CoRR, csAI/9501101. 1995. [link]
• Geoffrey I Webb. Learning Decision Lists by Prepending Inferred Rules. School of Computing and Mathematics Deakin University. [link]
• Bernhard Pfahringer and Ian H. Witten and Philip Chan. Improving Bagging Performance by Increasing Decision Tree Diversity. Austrian Research Institute for AI. [link]
• D. Randall Wilson and Roel Martinez. Improved Center Point Selection for Probabilistic Neural Networks. Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms. [link]
• Marcus Hutter and Marco Zaffalon. Distribution of Mutual Information from Complete and Incomplete Data. CoRR, csLG/0403025. 2004. [link]
• Mark A. Hall. Department of Computer Science Hamilton, NewZealand Correlation-based Feature Selection for Machine Learning. Doctor of Philosophy at The University of Waikato. 1999. [link]
• Jerome H. Friedman and Ron Kohavi and Youngkeol Yun. To appear in AAAI-96 Lazy Decision Trees. Statistics Department and Stanford Linear Accelerator Center Stanford University. [link]
• Alexander K. Seewald. Dissertation Towards Understanding Stacking Studies of a General Ensemble Learning Scheme ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Naturwissenschaften. [link]
• Vassilis Athitsos and Stan Sclaroff. Boosting Nearest Neighbor Classifiers for Multiclass Recognition. Boston University Computer Science Tech. Report No, 2004-006. 2004. [link]
• Mohammed Waleed Kadous. Expanding the Scope of Concept Learning Using Metafeatures. School of Computer Science and Engineering, University of New South Wales. [link]
• Geoffrey I. Webb. OPUS: An Efficient Admissible Algorithm for Unordered Search. J. Artif. Intell. Res. (JAIR, 3. 1995. [link]
• Alexander K. Seewald. Meta-Learning for Stacked Classification. Austrian Research Institute for Artificial Intelligence. [link]
• Mohammed Waleed Kadous and Claude Sammut. The University of New South Wales School of Computer Science and Engineering Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series. [link]
• Jihoon Yang and Rajesh Parekh and Vasant Honavar. DistAl: An inter-pattern distance-based constructive learning algorithm. Intell. Data Anal, 3. 1999. [link]
• Pedro Domingos. Unifying Instance-Based and Rule-Based Induction. Machine Learning, 24. 1996. [link]
• Wai Lam and Kin Keung and Charles X. Ling. PR 1527. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. 2001. [link]
• Alexander K. Seewald. How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness. ICML. 2002. [link]
• Richard Nock and Marc Sebban and David Bernard. A SIMPLE LOCALLY ADAPTIVE NEAREST NEIGHBOR RULE WITH APPLICATION TO POLLUTION FORECASTING. International Journal of Pattern Recognition and Artificial Intelligence Vol. 2003. [link]
• Alexander K. Seewald and Johann Petrak and Gerhard Widmer. Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study. FLAIRS Conference. 2001. [link]
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