zbMATH — the first resource for mathematics

Examples
Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

Operators
a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
Fields
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Rough sets and Boolean reasoning. (English) Zbl 1142.68551
Summary: We discuss methods based on the combination of rough sets and Boolean reasoning with applications in pattern recognition, machine learning, data mining and conflict analysis.

MSC:
68T37Reasoning under uncertainty
68T05Learning and adaptive systems
68T10Pattern recognition, speech recognition
WorldCat.org
Full Text: DOI
References:
[1] Alpigini, J. J.; Peters, J. F.; Skowron, A.; Zhong, N.: Third international conference on rough sets and current trends in computing (RSCTC’2002), malvern, PA, October 14 -- 16, 2002. Lecture notes in artificial intelligence 2475 (2002) · Zbl 1001.00048
[2] A. An, Y. Huang, X. Huang, N. Cercone, Feature selection with rough sets for web page classification, in: Peters et al. [95], pp. 1 -- 13. · Zbl 1108.68607
[3] Banerjee, M.; Pal, S. K.: Roughness of a fuzzy set. Information sciences 93, No. 3 -- 4, 235-246 (1996) · Zbl 0879.04004
[4] J. Bazan, R. Latkowski, M. Szczuka, DIXER -- Distributed executor for rough set exploration system, in: Śle¸zak et al. [121], pp. 362 -- 371.
[5] J. Bazan, H.S. Nguyen, S.H. Nguyen, P. Synak, J. Wróblewski, Rough set algorithms in classification problems, in: Polkowski et al. [98], pp. 49 -- 88. · Zbl 0992.68197
[6] J. Bazan, A. Osmólski, A. Skowron, D. Śle¸zak, M. Szczuka, J. Wróblewski, Rough set approach to the survival analysis, in: Alpigini et al. [1], pp. 522 -- 529. · Zbl 1013.68833
[7] J. Bazan, A. Skowron, On-line elimination of non-relevant parts of complex objects in behavioral pattern identification, in: Pal et al. [82], pp. 720 -- 725.
[8] Bazan, J.; Skowron, A.; Śle&cedil, D.; Zak; Wróblewski, J.: Searching for the complex decision reducts: the case study of the survival analysis. Lecture notes in artificial intelligence 2871, 160-168 (2003) · Zbl 1070.68636
[9] Bazan, J.; Szczuka, M.; Wojna, M.; Wojnarski, M.: On the evolution of rough set exploration system. Lecture notes in artificial intelligence 3066, 592-601 (2004) · Zbl 1103.68671
[10] J.G. Bazan, A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables, in: Polkowski and Skowron [99], pp. 321 -- 365. · Zbl 1067.68711
[11] J.G. Bazan, H.S. Nguyen, J.F. Peters, A. Skowron, M. Szczuka, Rough set approach to pattern extraction from classifiers, in: A. Skowron, M. Szczuka (Eds.), Proceedings of the Workshop on Rough Sets in Knowledge Discovery and Soft Computing at ETAPS 2003, April 12 -- 13, 2003, Electronic Notes in Computer Science, vol. 82 (4), Elsevier, Amsterdam, Netherlands, 2003, pp. 20 -- 29. Available from: <http://www.elsevier.nl/locate/entcs/volume82.html>. · Zbl 1270.68306
[12] J.G. Bazan, J.F. Peters, A. Skowron, Behavioral pattern identification through rough set modelling, in: Śle¸zak et al. [121], pp. 688 -- 697.
[13] Bazan, J. G.; Skowron, A.: Classifiers based on approximate reasoning schemes. Monitoring, security, and rescue tasks in multiagent systems (MSRAS’2004), advances in soft computing, 191-202 (2005) · Zbl 1082.68832
[14] J.G. Bazan, M. Szczuka, RSES and RSESlib -- a collection of tools for rough set computations, in: Ziarko and Yao [137], pp. 106 -- 113. · Zbl 1014.68825
[15] Brown, F.: Boolean reasoning. (1990) · Zbl 0719.03002
[16] Casti, J. L.: Alternate realities? mathematical models of nature and man. (1989) · Zbl 0676.00028
[17] Chmielewski, M. R.; Grzymała-Busse, J. W.: Global discretization of continuous attributes as preprocessing for machine learning. International journal of approximate reasoning 15, No. 4, 319-331 (1996) · Zbl 0949.68560
[18] Choubey, S. K.; Deogun, J. S.; Raghavan, V. V.; Sever, H.: A comparison of feature selection algorithms in the context of rough classifiers. International conference on fuzzy systems (FUZZ-IEEE’1996), September 8 -- 11, 1996, new Orléans, LA 2, 1122-1128 (1996)
[19] Coombs, C. H.; Avruin, G. S.: The structure of conflicts. (1988)
[20] R. Deja, Conflict analysis, rough set methods and applications, in: Polkowski et al. [98], pp. 491 -- 520.
[21] Deja, R.; Skowron, A.: On some conflict models and conflict resolution. Romanian journal of information science and technology 5, No. 1 -- 2, 69-82 (2002)
[22] J. Deogun, V.V. Raghavan, A. Sarkar, H. Sever, Data mining: Trends in research and development, in: Lin and Cercone [57], pp. 9 -- 46.
[23] Doherty, P.; łukaszewicz, W.; Skowron, A.; Szałas, A.: Knowledge engineering: A rough set approach. Studies in fuzziness and soft computing 202 (2006) · Zbl 1131.68107
[24] V. Dubois, M. Quafafou, Concept learning with approximation: rough version spaces, in: Alpigini et al. [1], pp. 239 -- 246. · Zbl 1013.68574
[25] Duda, R.; Hart, P.; Stork, R.: Pattern classification. (2002) · Zbl 0968.68140
[26] Düntsch, I.; Gediga, G.: Rough set data analysis: A road to non-invasive knowledge discovery. (2000)
[27] Fedrizzi, M.; Kacprzyk, J.; Nurmi, H.: How different are social choice functions: A rough sets approach. Quality and quantity 30, 87-99 (1996) · Zbl 0943.90588
[28] Friedman, J. H.; Hastie, T.; Tibshirani, R.: The elements of statistical learning: data mining, inference, and prediction. (2001) · Zbl 0973.62007
[29] Gediga, G.; Düntsch, I.: Rough approximation quality revisited. Artificial intelligence 132, 219-234 (2001) · Zbl 0983.68194
[30] G. Gediga, I. Düntsch, On model evaluation, indices of importance, and interaction values in rough set analysis, in: Pal et al. [87], pp. 251 -- 276.
[31] Góra, G.; Wojna, A. G.: RIONA: A new classification system combining rule induction and instance-based learning. Fundamenta informaticae 51, No. 4, 369-390 (2002) · Zbl 1011.68114
[32] Greco, S.; Inuiguchi, M.; Słowiński, R.: Fuzzy rough sets and multiple-premise gradual decision rules. International journal of approximate reasoning 41, No. 2, 179-211 (2006) · Zbl 1093.68114
[33] Grzymała-Busse, J. W.: Selected algorithms of machine learning from examples. Fundamenta informaticae 18, 193-207 (1993)
[34] Grzymała-Busse, J. W.: Classification of unseen examples under uncertainty. Fundamenta informaticae 30, No. 3 -- 4, 255-267 (1997)
[35] Grzymała-Busse, J. W.: A new version of the rule induction system LERS. Fundamenta informaticae 31, No. 1, 27-39 (1997) · Zbl 0882.68122
[36] J.W. Grzymała-Busse, Three strategies to rule induction from data with numerical attributes, in: Peters et al. [95], pp. 54 -- 62. · Zbl 1108.68611
[37] J.W. Grzymała-Busse, LERS -- A data mining system, in: Maimon and Rokach [62], pp. 1347 -- 1351.
[38] J.W. Grzymała-Busse, Rule induction, in: Maimon and Rokach [62], pp. 277 -- 294.
[39] J.W. Grzymała-Busse, W.J. Grzymała-Busse, Handling missing attribute values, in: Maimon and Rokach [62], pp. 37 -- 57.
[40] Grzymała-Busse, J. W.; Ziarko, W.: Data mining and rough set theory. Communications of the ACM 43, 108-109 (2000)
[41] J. Herbert, J.T. Yao, Time-series data analysis with rough sets, in: Proceedings of the 4th International Conference on Computational Intelligence in Economics and Finance (CIEF’2005), Salt Lake City, UT, July 21 -- 26, 2005, pp. 908 -- 911.
[42] Hu, X.; Cercone, N.: Learning in relational databases: a rough set approach. Computational intelligence: an international journal 11, No. 2, 323-338 (1995)
[43] Hu, X.; Cercone, N.: Data mining via discretization, generalization and rough set feature selection. Knowledge and information systems: an international journal 1, No. 1, 33-60 (1999)
[44] Hu, X.; Cercone, N.: Discovering maximal generalized decision rules through horizontal and vertical data reduction. Computational intelligence: an international journal 17, No. 4, 685-702 (2001)
[45] Kim, D.: Data classification based on tolerant rough set. Pattern recognition 34, No. 8, 1613-1624 (2001) · Zbl 0984.68520
[46] Kim, D.; Bang, S. Y.: A handwritten numeral character classification using tolerant rough set. IEEE transactions on pattern analysis and machine intelligence 22, No. 9, 923-937 (2000)
[47] Kloesgen, W.; &zdot, J.; Ytkow: Handbook of knowledge discovery and data mining. (2002)
[48] Komorowski, J.; øhrn, A.; Skowron, A.: Rosetta and other software systems for rough sets. Handbook of data mining and knowledge discovery, 554-559 (2000)
[49] R. Kowalski. A logic-based approach to conflict resolution. Report, Department of Computing, Imperial College, 2003, pp. 1 -- 28. Available from: URL <http://www.doc.ic.ac.uk/ rak/papers/conflictresolution.pdf>.
[50] Kraus, S.: Strategic negotiations in multiagent environments. (2001) · Zbl 1062.68099
[51] Kryszkiewicz, M.; Rybinski, H.: Computation of reducts of composed information systems. Fundamenta informaticae 27, No. 2 -- 3, 183-195 (1996) · Zbl 0854.68097
[52] G. Lai, C. Li, K. Sycara, J.A. Giampapa. Literature review on multi-attribute negotiations. Technical Report CMU-RI-TR-04-66, 2004, pp. 1 -- 35.
[53] Latkowski, R.: On decomposition for incomplete data. Fundamenta informaticae 54, No. 1, 1-16 (2003) · Zbl 1146.68460
[54] Latkowski, R.: Flexible indiscernibility relations for missing attribute values. Fundamenta informaticae 67, No. 1 -- 3, 131-147 (2005) · Zbl 1096.68149
[55] J. Li, N. Cercone, A rough set based model to rank the importance of association rules, in: Śle¸zak et al. [121], pp. 109 -- 118.
[56] Li, Y.; Shiu, S. C. -K.; Pal, S. K.; Liu, J. N. -K.: A rough set-based case-based reasoner for text categorization. International journal of approximate reasoning 41, No. 2, 229-255 (2006)
[57] Lin, T. Y.; Cercone, N.: Rough sets and data mining -- analysis of imperfect data. (1997) · Zbl 0855.00039
[58] Lingras, P.: Fuzzy -- rough and rough -- fuzzy serial combinations in neurocomputing. Neurocomputing 36, No. 1 -- 4, 29-44 (2001) · Zbl 1003.68637
[59] Lingras, P.: Unsupervised rough set classification using gas. Journal of intelligent information systems 16, No. 3, 215-228 (2001) · Zbl 1016.68112
[60] Lingras, P.; West, C.: Interval set clustering of web users with rough K-means. Journal of intelligent information systems 23, No. 1, 5-16 (2004) · Zbl 1074.68586
[61] Y. Maeda, K. Senoo, H. Tanaka, Interval density function in conflict analysis, in: Skowron et al. [110], pp. 382 -- 389.
[62] Maimon, O.; Rokach, L.: The data mining and knowledge discovery handbook. (2005) · Zbl 1087.68029
[63] T.M. Mitchel, Machine Learning, McGraw-Hill Series in Computer Science, Boston, MA, 1999.
[64] P. Mitra, S. Mitra, S.K. Pal, Modular rough fuzzy mlp: evolutionary design, in: Skowron et al. [110], pp. 128 -- 136.
[65] Mitra, P.; Pal, S. K.; Siddiqi, M. A.: Non-convex clustering using expectation maximization algorithm with rough set initialization. Pattern recognition letters 24, No. 6, 863-873 (2003) · Zbl 1053.68098
[66] A. Nakamura, Conflict logic with degrees, in: Pal and Skowron [88], pp. 136 -- 150.
[67] M. Nakata, H. Sakai, Rough sets handling missing values probabilistically interpreted, in: Śle¸zak et al. [120], pp. 325 -- 334. · Zbl 1134.68548
[68] H.S. Nguyen. Discretization of Real Value Attributes, Boolean Reasoning Approach. Ph.D. thesis, Warsaw University, Warsaw, Poland, 1997.
[69] Nguyen, H. S.: From optimal hyperplanes to optimal decision trees. Fundamenta informaticae 34, No. 1 -- 2, 145-174 (1998) · Zbl 0903.68161
[70] Nguyen, H. S.: Efficient SQL-learning method for data mining in large data bases. Sixteenth international joint conference on artificial intelligence IJCAI, 806-811 (1999)
[71] Nguyen, H. S.: On efficient handling of continuous attributes in large data bases. Fundamenta informaticae 48, No. 1, 61-81 (2001) · Zbl 0997.68037
[72] Nguyen, H. S.; Nguyen, S. H.: Pattern extraction from data. Fundamenta informaticae 34, 129-144 (1998) · Zbl 0903.68054
[73] Nguyen, H. S.; Nguyen, S. H.: Rough sets and association rule generation. Fundamenta informaticae 40, No. 4, 383-405 (1999) · Zbl 0946.68153
[74] H.S. Nguyen, A. Skowron, Quantization of real value attributes, in: Proceedings of the Second Joint Annual Conference on Information Sciences, Wrightsville Beach, NC, USA, 1995, pp. 34 -- 37.
[75] H.S. Nguyen, D. Śle¸zak, Approximate reducts and association rules -- correspondence and complexity results, in: Skowron et al. [110], pp. 137 -- 145.
[76] S.H. Nguyen, Regularity analysis and its applications in data mining, in: Polkowski et al. [98], pp. 289 -- 378. · Zbl 0992.68049
[77] S.H. Nguyen, J. Bazan, A. Skowron, H.S. Nguyen, Layered learning for concept synthesis, in: Peters and Skowron [94], pp. 187 -- 208. · Zbl 1104.68565
[78] S.H. Nguyen, H.S. Nguyen, Some efficient algorithms for rough set methods, in: Sixth International Conference on Information Processing and Management of Uncertainty on Knowledge Based Systems IPMU’1996, Granada, Spain, vol. III, 1996, pp. 1451 -- 1456.
[79] T.T. Nguyen, Eliciting domain knowledge in handwritten digit recognition, in: Pal et al. [82], pp. 762 -- 767.
[80] T.T. Nguyen, A. Skowron, Rough set approach to domain knowledge approximation, in: Wang et al. [131], pp. 221 -- 228. · Zbl 1026.68644
[81] Nurmi, H.; Kacprzyk, J.; Fedrizzi, M.: Theory and methodology: probabilistic, fuzzy and rough concepts in social choice. European journal of operational research 95, 264-277 (1996) · Zbl 0943.90588
[82] Pal, S. K.; Bandoyopadhay, S.; Biswas, S.: First international conference on pattern recognition and machine intelligence (PReMI’05) December 18 -- 22, 2005, indian statistical institute, Kolkata. Lecture notes in computer science 3776 (2005)
[83] Pal, S. K.; Dasgupta, B.; Mitra, P.: Rough self organizing map. Applied intelligence 21, 289-299 (2004) · Zbl 1101.68825
[84] Pal, S. K.; Mitra, P.: Case generation using rough sets with fuzzy representation. IEEE transactions on knowledge and data engineering 16, No. 3, 292-300 (2004)
[85] Pal, S. K.; Mitra, P.: Pattern recognition algorithms for data mining. (2004) · Zbl 1099.68091
[86] S.K. Pal, W. Pedrycz, A. Skowron, R. Swiniarski (Eds.), Special Volume: Rough-neuro Computing, Neurocomputing, vol. 36, 2001.
[87] Pal, S. K.; Polkowski, L.; Skowron, A.: Rough-neural computing: techniques for computing with words, cognitive technologies. (2004) · Zbl 1040.68113
[88] Pal, S. K.; Skowron, A.: Rough fuzzy hybridization: A new trend in decision-making. (1999) · Zbl 0941.68129
[89] Pawlak, Z.: Rough sets: theoretical aspects of reasoning about data, system theory, knowledge engineering and problem solving. 9 (1991) · Zbl 0758.68054
[90] Pawlak, Z.: An inquiry into anatomy of conflicts. Journal of information sciences 109, 65-78 (1998)
[91] Z. Pawlak, A. Skowron, Rough sets: some extensions, Information Sciences, in press, doi:10.1016/j.ins.2006.06.006. · Zbl 1142.68550
[92] Z. Pawlak, A. Skowron, Rudiments of rough sets, Information Sciences, in press, doi:10.1016/j.ins.2006.06.003. · Zbl 1142.68549
[93] Z. Pawlak, L. Polkowski, A. Skowron, Rough sets and rough logic: a KDD perspective, in: Polkowski et al. [98], pp. 583 -- 646. · Zbl 1009.68159
[94] Peters, J. F.; Skowron, A.: Transactions on rough sets I: Journal subline. Lecture notes in computer science 3100 (2004)
[95] Peters, J. F.; Skowron, A.; Dubois, D.; Grzymała-Busse, J. W.; Inuiguchi, M.; Polkowski, L.: Transactions on rough sets II. Rough sets and fuzzy sets: journal subline. Lecture notes in computer science 3135 (2004) · Zbl 1062.68008
[96] Peters, J. F.; Skowron, A.; Suraj, Z.: An application of rough set methods in control design. Fundamenta informaticae 43, No. 1 -- 4, 269-290 (2000) · Zbl 0971.93052
[97] Peters, J. F.; Suraj, Z.; Shan, S.; Ramanna, S.; Pedrycz, W.; Pizzi, N. J.: Classification of meteorological volumetric radar data using rough set methods. Pattern recognition letters 24, No. 6, 911-920 (2003)
[98] Polkowski, L.; Lin, T. Y.; Tsumoto, S.: Rough set methods and applications: new developments in knowledge discovery in information systems, studies in fuzziness and soft computing. 56 (2000)
[99] Polkowski, L.; Skowron, A.: Rough sets in knowledge discovery 1: methodology and applications, studies in fuzziness and soft computing. 18 (1998) · Zbl 0910.00028
[100] Polkowski, L.; Skowron, A.: Rough sets in knowledge discovery 2: applications, case studies and software systems, studies in fuzziness and soft computing. 19 (1998) · Zbl 0910.00029
[101] Quafafou, M.; Boussouf, M.: Generalized rough sets based feature selection. Intelligent data analysis 4, No. 1, 3-17 (2000) · Zbl 1055.68560
[102] Roy, A.; Pal, S. K.: Fuzzy discretization of feature space for a rough set classifier. Pattern recognition letters 24, No. 6, 895-902 (2003) · Zbl 1053.68091
[103] Sever, H.; Raghavan, V. V.; Johnsten, T. D.: The status of research on rough sets for knowledge discovery in databases. Proceedings of the second internationall conference on nonlinear problems in aviation and aerospace (ICNPAA’1998), April 29 -- May 1, 1998, daytona beach, FL 2, 673-680 (1998)
[104] Shan, N.; Ziarko, W.: An incremental learning algorithm for constructing decision rules. Rough sets, fuzzy sets and knowledge discovery, 326-334 (1994) · Zbl 0941.68698
[105] Skowron, A.: Synthesis of adaptive decision systems from experimental data. Frontiers in artificial intelligence and applications 28, 220-238 (1995)
[106] Skowron, A.: Rough sets in KDD -- plenary talk. 16th world computer congress (IFIP’2000): Proceedings of conference on intelligent information processing (IIP’2000), 1-14 (2000)
[107] Skowron, A.: Rough sets and Boolean reasoning. Studies in fuzziness and soft computing 70, 95-124 (2001) · Zbl 0986.68143
[108] Skowron, A.: Approximate reasoning in distributed environments. Intelligent technologies for information analysis, 433-474 (2004)
[109] Skowron, A.; Nguyen, H. S.: Boolean reasoning scheme with some applications in data mining. Lecture notes in computer science 1704, 107-115 (1999)
[110] Skowron, A.; Ohsuga, S.; Zhong, N.: Proceedings of the 7th international workshop on rough sets, fuzzy sets, data mining, and granular-soft computing (RSFDGrC’99), Yamaguchi, November 9 -- 11, 1999. Lecture notes in artificial intelligence 1711 (1999) · Zbl 0929.00075
[111] A. Skowron, S.K. Pal (Eds.), Special volume: Rough sets, pattern recognition and data mining, Pattern Recognition Letters, vol. 24 (6), 2003.
[112] Skowron, A.; Pawlak, Z.; Komorowski, J.; Polkowski, L.: A rough set perspective on data and knowledge. Handbook of KDD, 134-149 (2002)
[113] A. Skowron, J. Peters, Rough sets: trends and challenges, in: Wang et al. [131], pp. 25 -- 34 (plenary talk). · Zbl 1026.68653
[114] Skowron, A.; Rauszer, C.: The discernibility matrices and functions in information systems. System theory, knowledge engineering and problem solving 11, 331-362 (1992)
[115] A. Skowron, J. Stepaniuk, Information granules and rough-neural computing, in: Pal et al. [87], pp. 43 -- 84.
[116] A. Skowron, J. Stepaniuk, Ontological framework for approximation, in: Śle¸zak et al. [120], pp. 718 -- 727.
[117] Śle&cedil, D.; Zak: Association reducts: a framework for mining multi-attribute dependencies. Lecture notes in artificial intelligence 3488, 354-363 (2005) · Zbl 1132.68592
[118] D. Śle¸zak, Various approaches to reasoning with frequency-based decision reducts: a survey, in: Polkowski et al. [98], pp. 235 -- 285.
[119] Śle&cedil, D.; Zak: Approximate entropy reducts. Fundamenta informaticae 53, 365-387 (2002)
[120] Śle&cedil, D.; Zak; Wang, G.; Szczuka, M.; Düntsch, I.; Yao, Y.: Proceedings of the 10th international conference on rough sets, fuzzy sets, data mining, and granular computing (RSFDGrC’2005), Regina, Canada, August 31 -- September 3, 2005, part I. Lecture notes in artificial intelligence 3641 (2005)
[121] Śle&cedil, D.; Zak; Yao, J. T.; Peters, J. F.; Ziarko, W.; Hu, X.: Proceedings of the 10th international conference on rough sets, fuzzy sets, data mining, and granular computing (RSFDGrC’2005), Regina, Canada, August 31 -- September 3, 2005, part II. Lecture notes in artificial intelligence 3642 (2005)
[122] J. Stepaniuk, Knowledge discovery by application of rough set models, in: Polkowski et al. [98], pp. 137 -- 233. · Zbl 0992.68199
[123] Z. Suraj, Rough set methods for the synthesis and analysis of concurrent processes, in: Polkowski et al. [98], pp. 379 -- 488.
[124] R. Swiniarski, Rough sets and principal component analysis and their applications. data model building and classification, in: Pal and Skowron [88], pp. 275 -- 300.
[125] R. Swiniarski, An application of rough sets and Haar wavelets to face recognition, in: Ziarko and Yao [137], pp. 561 -- 568. · Zbl 1013.68261
[126] R. Swiniarski, L. Hargis, A new halftoning method based on error diffusion with rough set filterin, in: Polkowski and Skowron [100], pp. 336 -- 342.
[127] Swiniarski, R.; Skowron, A.: Rough set methods in feature selection and extraction. Pattern recognition letters 24, No. 6, 833-849 (2003) · Zbl 1053.68093
[128] R.W. Swiniarski, A. Skowron, Independent component analysis, principal component analysis and rough sets in face recognition, in: Peters and Skowron [94], pp. 392 -- 404. · Zbl 1104.68772
[129] Sycara, K.: Multiagent systems. AI magazine, 79-92 (1998)
[130] Tsumoto, S.; Tanaka, H.: PRIMEROSE: probabilistic rule induction method based on rough sets and resampling methods. Computational intelligence: an international journal 11, 389-405 (1995)
[131] Wang, G.; Liu, Q.; Yao, Y.; Skowron, A.: Proceedings of the 9th international conference on rough sets, fuzzy sets, data mining, and granular computing (RSFDGrC’2003), Chongqing, China, May 26 -- 29, 2003. Lecture notes in artificial intelligence 2639 (2003) · Zbl 1019.00015
[132] Wang, J.; Jia, C.; Zhao, K.: Investigation on AQ11, ID3 and the principle of discernibility matrix. Journal of computer science and technology 16, No. 1, 1-12 (2001) · Zbl 0974.68175
[133] Wojna, A.: Analogy based reasoning in classifier construction. Lecture notes in computer science 3700, 277-374 (2005) · Zbl 1136.68508
[134] Wróblewski, J.: Theoretical foundations of order-based genetic algorithms. Fundamenta informaticae 28, 423-430 (1996) · Zbl 0866.68043
[135] J. Wróblewski, Analyzing relational databases using rough set based methods, in: Eighth International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems IPMU. Madrid, Spain, vol. I, 2000, pp. 256 -- 262.
[136] J. Wróblewski, Adaptive aspects of combining approximation spaces, in: Pal et al. [87], pp. 139 -- 156.
[137] Ziarko, W.; Yao, Y.: Proceedings of the 2nd international conference on rough sets and current trends in computing (RSCTC’2000), Banff, Canada, October 16 -- 19, 2000. Lecture notes in artificial intelligence 2005 (2001)