Bibliographic record
Abstract
This volume contains the papers selected for presentation at the Rough Sets in Knowledge Discovery and Soft Computing Workshop (RSKD'2003), held in Warsaw, Poland, April 12-13, 2003. The workshop was organized as one of the satellite events of the European Conference on Theory and Practice of Software (ETAPS'2003). We would like to express our thanks to Professor Damian Niwinski, ETAPS'2003 Workshop Chair, for his invitation to organize the workshop. It is our great pleasure to dedicate this proceedings to Professor Zdzisaw Pawlak, the honorary chair of the RSKD'2003 workshop, who created rough set theory over twenty years ago. We would like also to thank him for enriching our event with his invited talk. In recent years, there have been a number of advances in rough set theory and its applications. Hence, we have witnessed a growing number of international workshops and conferences on rough sets and their applications. Many international conferences are now including rough sets into the list of topics. The RSKD'2003 workshop was intended as a forum for exchanging ideas among experts in rough set theory and its applications, especially in rapidly growing areas like Knowledge Discovery and Data Mining as well as in Soft Computing. The papers, submitted from Canada, China, Great Britain, France, India, Italy, Japan, Russia, Sweden, United States, and Poland, were selected by Program Committee. We would like to express our appreciation to all who submitted papers for presentation and publication in proceedings. Many thanks to the Program Committee Members for reviewing the submitted papers. Special thanks are due to Michael Mislove and Elsevier Publishers for making it possible to include our proceedings in Electronic Notes in Theoretical Computer Science and to Warsaw University for printing the hard copy of our proceedings. April, 2003 Andrzej Skowron and Marcin Szczuka RSKD 2003 Workshop Committee Honorary Chair: Zdzislaw Pawlak Program Chair: Andrzej Skowron Workshop Chair: Marcin Szczuka Program Committee James Alpigini (USA) Malcolm Beynon (UK) Hans Dieter Burkhard (Germany) Andrzej Czyzewski(Poland) Patrick Doherty (Sweden) Ivo Dïntsch (Canada) Maria C. Fernandez (Spain) Jerzy Grzymaa-Busse (USA) Masahiro Inuiguchi (Japan) Jouni Järvinen (Finland) Jan Komorowski (Sweden) Jacek Koronacki (Poland) Bozena Kostek (Poland) Tsau Young Lin (USA) Ernestina Menasalvas-Ruiz (Spain) Mikhail Moshkov (Russia) Tetsuya Murai (Japan) Hung Son Nguyen (Poland) Sinh Hoa Nguyen (Poland) Ewa Orowska (Poland) Sankar K. Pal (India) Witold Pedrycz (Canada) James F. Peters (Canada) Lech Polkowski (Poland) Sheela Ramanna (Canada) Zbigniew E. Ras (USA) Roman Slowinski (Poland) Jerzy Stefanowski (Poland) Jaroslaw Stepaniuk (Poland) Zbigniew Suraj (Poland) Andrzej Szaas (Poland) Marcin Szczuka (Poland) Domik Szlezak (Poland) Roman Swiniarski (USA) Shusaku Tsumoto (Japan) Guoyin Wang (China) Jakub Wróblewski (Poland) Yiyu Yao (Canada) Ning Zhong (Japan) Wojciech Ziarko (Canada).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.544 | 0.419 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".