Robert Susmaga
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2010 – today
- 2018
- [j13]Dariusz Brzezinski, Jerzy Stefanowski, Robert Susmaga, Izabela Szczech:
Visual-based analysis of classification measures and their properties for class imbalanced problems. Inf. Sci. 462: 242-261 (2018) - 2017
- [c13]Dariusz Brzezinski, Jerzy Stefanowski, Robert Susmaga, Izabela Szczech:
Tetrahedron: Barycentric Measure Visualizer. ECML/PKDD (3) 2017: 419-422 - [i1]Dariusz Brzezinski, Jerzy Stefanowski, Robert Susmaga, Izabela Szczech:
Visual-Based Analysis of Classification Measures with Applications to Imbalanced Data. CoRR abs/1704.07122 (2017) - 2016
- [j12]Robert Susmaga, Izabela Szczech:
Selected group-theoretic aspects of confirmation measure symmetries. Inf. Sci. 346-347: 424-441 (2016) - 2015
- [j11]Robert Susmaga, Izabela Szczech:
Can Interestingness Measures be Usefully Visualized? Applied Mathematics and Computer Science 25(2): 323-336 (2015) - [j10]Robert Susmaga, Roman Slowinski:
Generation of rough sets reducts and constructs based on inter-class and intra-class information. Fuzzy Sets and Systems 274: 124-142 (2015) - 2014
- [j9]Robert Susmaga:
Reducts and constructs in classic and dominance-based rough sets approach. Inf. Sci. 271: 45-64 (2014) - [c12]Robert Susmaga, Izabela Szczech:
Visual-Based Detection of Properties of Confirmation Measures. ISMIS 2014: 133-143 - 2013
- [c11]Robert Susmaga, Izabela Szczech:
The Property of $\chi^2_{01}$ -Concordance for Bayesian Confirmation Measures. MDAI 2013: 226-236 - 2012
- [c10]Przemyslaw Gawel, Krzysztof Dembczynski, Robert Susmaga, Przemyslaw Wesolek, Piotr Zielniewicz, Andrzej Jaszkiewicz:
Adapting Travel Time Estimates to Current Traffic Conditions. ADBIS Workshops 2012: 79-88 - [c9]Przemyslaw Gawel, Krzysztof Dembczynski, Wojciech Kotlowski, Marek Kubiak, Robert Susmaga, Piotr Zielniewicz, Andrzej Jaszkiewicz:
Community Traffic: A Technology for the Next Generation Car Navigation. ADBIS Workshops 2012: 339-348 - 2011
- [c8]Jerzy Blaszczynski, Roman Slowinski, Robert Susmaga:
Rule-Based Estimation of Attribute Relevance. RSKT 2011: 36-44
2000 – 2009
- 2004
- [j8]Roman Pindur, Robert Susmaga, Jerzy Stefanowski:
Hyperplane Aggregation of Dominance Decision Rules. Fundam. Inform. 61(2): 117-137 (2004) - [j7]Robert Susmaga:
Reducts and Constructs in Attribute Reduction. Fundam. Inform. 61(2): 159-181 (2004) - [c7]Luiza Budzynska, Jacek Jelonek, Ewa Lukasik, Aleksander Naganowski, Roman Slowinski, Robert Susmaga:
Knowledge Discovery from Digital Media Objects Using Preference Semantics. EWIMT 2004 - [c6]
- [c5]Robert Susmaga:
Tree-Like Parallelization of Reduct and Construct Computation. Rough Sets and Current Trends in Computing 2004: 455-464 - 2003
- [j6]Krzysztof Dembczynski, Roman Pindur, Robert Susmaga:
Dominance-based Rough Set Classifier without Induction of Decision Rules. Electr. Notes Theor. Comput. Sci. 82(4): 84-95 (2003) - [j5]Krzysztof Dembczynski, Roman Pindur, Robert Susmaga:
Generation of Exhaustive Set of Rules within Dominance-based Rough Set Approach. Electr. Notes Theor. Comput. Sci. 82(4): 96-107 (2003) - [j4]Robert Susmaga:
Reducts Versus Constructs: an Experimental Evaluation. Electr. Notes Theor. Comput. Sci. 82(4): 239-250 (2003) - [j3]Roman Pindur, Robert Susmaga:
Fast rule extraction with binary-coded relations. Intell. Data Anal. 7(1): 27-42 (2003) - 2000
- [c4]Robert Susmaga:
Generation of Exhaustive Rule Sets Using a Reduct Generating Algorithm. Intelligent Information Systems 2000: 65-74
1990 – 1999
- 1999
- [j2]A. I. Dimitras, Roman Slowinski, Robert Susmaga, Constantin Zopounidis:
Business failure prediction using rough sets. European Journal of Operational Research 114(2): 263-280 (1999) - [c3]
- 1998
- [c2]Robert Susmaga:
Parallel Computation of Reducts. Rough Sets and Current Trends in Computing 1998: 450-457 - [c1]Bartlomiej Predki, Roman Slowinski, Jerzy Stefanowski, Robert Susmaga, Szymon Wilk:
ROSE - Software Implementation of the Rough Set Theory. Rough Sets and Current Trends in Computing 1998: 605-608 - 1997
- [j1]Robert Susmaga:
Analyzing Discretizations of Continuous Attributes Given a Monotonic Discrimination Function. Intell. Data Anal. 1(1-4): 157-179 (1997)
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