Aleksander Øhrn

发表

Discernibility and Rough Sets in Medicine: Tools and Applications

Aleksander Øhrn, Aleksander Øhrn, 2000 .

Minimal approximate hitting sets and rule templates

Staal A. Vinterbo, Aleksander Øhrn, Aleksander Øhrn, 2000, Int. J. Approx. Reason..

Modelling cardiac patient set residuals using rough sets

Jan Komorowski, Staal A. Vinterbo, Aleksander Øhrn, 1997, AMIA.

Case studies: Public domain, multiple mining tasks systems: ROSETTA rough sets

Andrzej Skowron, Jan Komorowski, Aleksander Øhrn, 2002 .

Multidimensional Visualization and Navigation in Search Results

Will Archer Arentz, Aleksander Øhrn, 2004, KES.

Comparison of multiple prediction models for ambulation following spinal cord injury

Lucila Ohno-Machado, Aleksander Øhrn, Todd Rowland, 1998, AMIA.

Building manageable rough set classifiers

Lucila Ohno-Machado, Aleksander Øhrn, Todd Rowland, 1998, AMIA.

Contextual Insight in Search: Enabling Technologies and Applications

Aleksander Øhrn, 2005, VLDB.

Book Review: Knowledge Discovery and Data Mining,Volume 1of IEE Professional Applications of Computing Series. Edited by M.A. Bramer, The Institution of Electrical Engineers, London, UK, 1999, p. 308+xix. ISBN 0-85296-767-5; hardcover, UK£48.00 (US $84.00)

Aleksander Øhrn, 2000 .

Improving machine learning performance by removing redundant cases in medical data sets

Lucila Ohno-Machado, Hamish S. F. Fraser, Aleksander Øhrn, 1998, AMIA.

Knowledge Discovery and Data Mining, Volume 1 of IEE Professional Applications of Computing Series, Edited by M.A. Bramer, IEE, London, UK, 1999, ISBN 0-85296-767-5

Aleksander Øhrn, 2000, Artif. Intell. Medicine.

Modelling prognostic power of cardiac tests using rough sets

Jan Komorowski, Aleksander Øhrn, J. Komorowski, 1999, Artif. Intell. Medicine.

Using Boolean reasoning to anonymize databases

Lucila Ohno-Machado, Aleksander Øhrn, L. Ohno-Machado, 1999, Artif. Intell. Medicine.

Some Heuristics for Default Knowledge Discovery

Jan Komorowski, Tor-Kristian Jenssen, Aleksander Øhrn, 1998, Rough Sets and Current Trends in Computing.

Clinical Data Processing Tools: A Machine Learning Resource

Lucila Ohno-Machado, Staal A. Vinterbo, Stephan Dreiseitl, 1999, AMIA.

R.ROSETTA: an interpretable machine learning framework

Jan Komorowski, Susanne Bornelöv, Mateusz Garbulowski, 2021, BMC Bioinform..