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SCI2S Publications (C. Cornelis)

Number of Results: 40

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In Press (1)

  • [2324] S. Vluymans, A. Fernandez, C. Cornelis, Y. Saeys, F. Herrera. Dynamic Affinity-based Classification of Multi-Class Imbalanced Data with One-vs-One Decomposition: a Fuzzy Rough Set Approach. Knowledge and Information Systems, in press. doi: 10.1007/s10115-017-1126-1 PDF Icon


2016 (5)


2015 (3)

  • [1846] E. Ramentol, S. Vluymans, N. Verbiest, Y. Caballero, R. Bello, C. Cornelis, F. Herrera. IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification. IEEE Transactions on Fuzzy Systems 23:5 (2015) 1622-1637. doi: 10.1109/TFUZZ.2014.2371472 PDF Icon
  • [1923] L. D’eer, N. Verbiest, C. Cornelis, L. Godo. A Comprehensive Study of Implicator-Conjunctor Based and Noise-Tolerant Fuzzy Rough Sets: Definitions, Properties and Robustness Analysis. Fuzzy Sets and Systems 275 (2015) 1-38. doi: 10.1016/j.fss.2014.11.018
  • [1930] I. Triguero, M. Galar, S. Vluymans, C. Cornelis, H. Bustince, F. Herrera, Y. Saeys. Evolutionary Undersampling for Imbalanced Big Data Classification. IEEE Congress on Evolutionary Computation (CEC 2015), Sendai (Japan), 715-722, May 25-28, 2015. PDF Icon


2014 (7)

  • [1667] M. Restrepo, C. Cornelis, J. Gómez. Duality, Conjugacy and Adjointness of Approximation Operators in Covering Based Rough Sets. International Journal of Approximate Reasoning 55:1 (2014) 469-485. doi: 10.1016/j.ijar.2013.08.002 PDF Icon
  • [1671] C. Cornelis, J. Medina, N. Verbiest. Multi-adjoint fuzzy rough sets: Definition, properties and attribute selection. International Journal of Approximate Reasoning 55:1 (2014) 412-426. doi: 10.1016/j.ijar.2013.09.007 PDF Icon
  • [1736] D.S. Tarragó, C. Cornelis, R. Bello, F. Herrera. A Multi-Instance Learning Wrapper based on the Rocchio Classifier for Web Index Recommendation. Knowledge-Based Systems 59 (2014) 173-181. doi: 10.1016/j.knosys.2014.01.008
  • [1745] L. D’eer, N. Verbiest, C. Cornelis, L. Godo. Modelos de Conjuntos Rugosos Difusos Tolerantes al Ruido: Definiciones y Propiedades. Proceedings of XVII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF2014), p. 27-32, 2014. PDF Icon
  • [1773] N. Verbiest, E. Ramentol, C. Cornelis, F. Herrera. Preprocessing Noisy Imbalanced Datasets using SMOTE enhanced with Fuzzy Rough Prototype Selection. Applied Soft Computing 22 (2014) 511-517. doi: 10.1016/j.asoc.2014.05.023 PDF Icon
  • [1817] L. S. Riza, Andrzej Janusz, C. Bergmeir, C. Cornelis, F. Herrera, Dominik Slezak, J.M. Benítez. Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets". Information Sciences 287 (2014) 68-89.
  • [1922] M. Restrepo, C. Cornelis, J. Gómez. Partial Order Relation for Approximation Operators in Covering Based Rough Sets. Information Sciences 284 (2014) 44-59. doi: 10.1016/j.ins.2014.06.032 PDF Icon


2013 (7)

  • [1537] J. Derrac, N. Verbiest, S. García, C. Cornelis, F. Herrera. On the use of Evolutionary Feature Selection for Improving Fuzzy Rough Set Based Prototype Selection. Soft Computing 17:2 (2013) 223-238. doi: 10.1007/s00500-012-0888-3 PDF Icon
  • [1562] G. Hurtado, S. Schockaert, C. Cornelis, H. Naessens. Using Semi-Structured Data for Assessing Research Paper Similarity. Information Sciences 221:1 (2013) 245-261. doi: /10.1016/j.ins.2012.09.044 PDF Icon
  • [1634] N. Verbiest, C. Cornelis, F. Herrera. FRPS: A Fuzzy Rough Prototype Selection method. Pattern Recognition 46:10 (2013) 2770-2782. doi: 10.1016/j.patcog.2013.03.004 PDF Icon
  • [1654] P. Victor, N. Verbiest, C. Cornelis, M. De Cock. Enhancing the Trust-Based Recommendation Process with Explicit Distrust. ACM Transactions on the Web 7(2) (2013). doi: 10.1145/2460383.2460385 PDF Icon
  • [1746] L. D’eer, N. Verbiest, C. Cornelis, L. Godo. Implicator-Conjunctor Based Models of Fuzzy Rough Sets: Definitions and Properties. Proceedings of the 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2013), LNAI 8170, p. 169-179, 2013. PDF Icon
  • [1747] M. Restrepo, C. Cornelis, J. Gómez. Characterization of Neighborhood Operators for Covering Based Rough Sets, using Duality and Adjointness. Proceedings of the 4th international workshop on Knowledge Discovery, Knowledge Management and Decision Support (EUREKA 2013), p. 98-104, 2013. PDF Icon
  • [1748] N. Verbiest, C. Cornelis, F. Herrera. OWA-FRPS: A Prototype Selection method based on Ordered Weighted Average Fuzzy Rough Set Theory. Proceedings of the 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2013), LNAI 8170, p. 180-190, 2013.. PDF Icon


2012 (7)

  • [1428] J. Derrac, C. Cornelis, S. García, F. Herrera. Enhancing Evolutionary Instance Selection Algorithms by means of Fuzzy Rough Set based Feature Selection. Information Sciences 186:1 (2012) 73-92. doi: 10.1016/j.ins.2011.09.027 PDF Icon
  • [1496] N. Verbiest, C. Cornelis, F. Herrera. Selección de Prototipos Basada en Conjuntos Rugosos Difusos. Proceedings of XVI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF2012). PDF Icon
  • [1534] N. Verbiest, C. Cornelis, P. Victor, E. Herrera-Viedma. Trust and distrust aggregation enhanced with path length incorporation. Fuzzy Sets and Systems , 202:1 (2012) 61–74. doi: 10.1016/j.fss.2012.02.007 PDF Icon
  • [1552] E. Ramentol, N. Verbiest, R. Bello, Y. Caballero, C. Cornelis, F. Herrera. SMOTE-FRST: A new resampling method using fuzzy rough set theory. The 10th International FLINS Conference on uncertainty Modeling in Knowledge Engineering and Decision Making, August 26-29, Istanbul, Turkey, pp. 800-805.
  • [1556] N. Verbiest, E. Ramentol, C. Cornelis, F. Herrera. Improving SMOTE with Fuzzy Rough Prototype Selection to Detect Noise in Imbalanced Classification Data. Proceedings of the 13th Ibero-American Conference on Artificial Intelligence (IBERAMIA 2012), Lecture Notes in Artificial Intelligence (LNAI) 7637, pp. 169-178, Cartagena de Indias, (Colombia), November 13-16, 2012. PDF Icon
  • [1616] B. Van Gasse, G. Deschrijver, C. Cornelis, E. E. Kerre. The Standard Completeness of Interval-Valued Monoidal t-Norm Based Logic. Information Sciences 189:1 (2012) 63-76. doi: 10.1016/j.ins.2011.11.043 PDF Icon
  • [1617] D. Mourisse, E. Lefever, N. Verbiest, Y. Saeys, M. De Cock, C. Cornelis. SBFC: An Efficient Feature Frequency-Based Approach to Tackle Cross-Lingual Word Sense Disambiguation. in: Proceedings of the 15th International Conference on Text, Speech and Dialogue (TSD2012), p. 248-255, 2012. PDF Icon


2011 (6)

  • [1325] P. Victor, C. Cornelis, M. De Cock, E. Herrera-Viedma. Practical Aggregation Operators for Gradual Trust and Distrust. Fuzzy Sets and Systems 184:1 (2011) 126-147. doi: 10.1016/j.fss.2010.10.015 PDF Icon
  • [1413] J. Derrac, C. Cornelis, S. García, F. Herrera. A Preliminary Study on the Use of Fuzzy Rough Set Based Feature Selection for Improving Evolutionary Instance Selection Algorithms. 11th International Work-Conference on Artificial Neural Networks (IWANN'11), Lecture Notes on Computer Science 6691, Torremolinos (Spain), pp. 174-182, June 8-10, 2011. PDF Icon
  • [1445] J. Derrac, S. García, C. Cornelis, F. Herrera. Una aplicación de conjuntos rugosos difusos en selección de características para la mejora de métodos de selección de instancias evolutivos. Actas de la XIV Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA11), Tenerife (Spain), November 7-11, 2011. PDF Icon
  • [1454] Richard Jensen, C. Cornelis. Fuzzy Rough Nearest Neighbour Classification and Prediction. Theoretical Computer Science 412:42 (2011) 5871-5884. doi: 10.1016/j.tcs.2011.05.040 PDF Icon
  • [1455] T. Fayruzov, J. Janssen, D. Vermeir, C. Cornelis, M. De Cock. Modelling Gene and Protein Regulatory Networks with Answer Set Programming. International Journal of Data Mining and Bioinformatics 5:2 (2011) 209-229. doi: 10.1504/IJDMB.2011.039178 PDF Icon
  • [1456] P. Victor, C. Cornelis, M. De Cock, A. Teredesai. Trust- and Distrust-Based Recommendations for Controversial Reviews. IEEE Intelligent Systems 26:1 (2011) 48-55. doi: 10.1109/MIS.2011.22 PDF Icon


2010 (3)

  • [1377] C. Cornelis, P. Victor, E. Herrera-Viedma. Ordered weighted averaging approaches for aggregating gradual trust and distrust. Proceedings of XV Congreso Español sobre Tecnologías y Lógica Fuzzy, 555-560 . PDF Icon
  • [1378] P. Victor, C. Cornelis, M. De Cock, E. Herrera-Viedma. Bilattice-Based Aggregation Operators for Gradual Trust and Distrust. Proceedings of the 9th International FLINS Conference on Computational Intelligence: Foundations and Applications, Chengdu (China), 505-510, 2-4 August 2010. PDF Icon
  • [1379] N. Verbiest, C. Cornelis, P. Victor, E. Herrera-Viedma. Strategies for Incorporating Knowledge Defects and Path Length in Trust Aggregation. Proceedings of The 23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2010), p. 450-459.


2009 (1)

  • [1210] P. Victor, C. Cornelis, M. De Cock, E. Herrera-Viedma. Aggregation of Gradual Trust and Distrust . EUROFUSE09 Workshop on Preference Modelling and Decision Analysis, Pamplona (Spain), 259-264, 16-18 September 2009.