R. Słowiński
Management Science and Operations Research · 2000–2020
A researcher in the Science corpus: 16 papers; trusts 30 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Management Science and Operations Research — 30 citations into it; #2,097 of 11,946 by trust here, #1,534 by citations
- Statistics and Probability — 20 citations into it; #11,510 of 12,932 by trust here, #7,914 by citations
- Artificial Intelligence — 19 citations into it; #11,613 of 35,345 by trust here, #15,236 by citations
- Information Systems — 10 citations into it; #2,140 of 8,771 by trust here, #3,012 by citations
- Control and Systems Engineering — 9 citations into it; #4,676 of 8,257 by trust here, #4,045 by citations
- Statistical Learning Theory — 8 citations into it
- Computational Theory and Mathematics — 7 citations into it; #1,014 of 12,182 by trust here, #761 by citations
- Economics and Econometrics — 7 citations into it
Whom R. Słowiński trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Salvatore Greco
- Jerome H. Friedman
- Amos Tversky
- Robert E. Schapire
- Arie Ben-David
- Yoav Freund
- Leo Breiman
- Vladimir Koltchinskii
- Eibe Frank
- Benedetto Matarazzo
Who trusts R. Słowiński overall
- Yee Leung
- Salvatore Greco
- William Zhu
- Witold Pedrycz
- Constantin Zopounidis
- Qinghua Hu
- Dmitry M. Malioutov
- Kannan Govindan
- Weizhi Wu
- Deyu Li
Papers from R. Słowiński's perspective
Ordered by how strongly the researchers R. Słowiński trusts endorse each paper. Same corpus, a different order for every researcher.
- Bagging Predictors
- Special Invited Paper-Additive logistic regression: A statistical view of boosting
- A decision-theoretic generalization of on-line learning and an application to boosting
- Greedy function approximation: A gradient boosting machine.
- The Elements of Statistical Learning
- An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
- Boosting the margin: A new explanation for the effectiveness of voting methods
- Improved Boosting Algorithms Using Confidence-rated Predictions
- Fast Effective Rule Induction
- The CN2 Induction Algorithm
Their papers
- A Generalized Definition of Rough Approximations Based on Similarity (2000) — 999 citations
- Forty years of the European Journal of Operational Research: A bibliometric overview (2017) — 296 citations
- Rough sets theory for multicriteria decision analysis (2001) — 1,646 citations
- Multiobjective Optimization (2008) — 705 citations
- Rough approximation by dominance relations (2002) — 473 citations
- A user-oriented implementation of the ELECTRE-TRI method integrating preference elicitation support (2000) — 312 citations
- ENDER: a statistical framework for boosting decision rules (2010) — 86 citations
- Rule learning with monotonicity constraints (2009) — 56 citations
- An Overview of ELECTRE Methods and their Recent Extensions (2013) — 373 citations
- Statistical Model for Rough Set Approach to Multicriteria Classification (2007) — 36 citations
- How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy (2020) — 311 citations
- Optimization of multiple satisfaction levels in portfolio decision analysis (2017) — 53 citations
- RUTA: a framework for assessing and selecting additive value functions on the basis of rank related requirements (2013) — 46 citations
- Rough Sets and Current Trends in Computing, 6th International Conference, RSCTC 2008, Akron, OH, USA, October 23-25, 2008, Proceedings (2008) — 339 citations
- Rough sets methodology for sorting problems in presence of multiple attributes and criteria (2002) — 417 citations
- Aiding decisions with multiple criteria: essays in honor of Bernard Roy (2002) — 88 citations