His scientific interests lie mostly in Rough set, Fuzzy set, Fuzzy logic, Artificial intelligence and Type-2 fuzzy sets and systems. His Rough set research is under the purview of Data mining. His work on Fuzzy set operations and Fuzzy number is typically connected to Information processing as part of general Fuzzy set study, connecting several disciplines of science.
His Fuzzy set operations research is multidisciplinary, incorporating elements of Discrete mathematics, Equivalence relation, Defuzzification and Fuzzy classification. His study in Fuzzy logic is interdisciplinary in nature, drawing from both Pure mathematics, Intuitionistic logic, Entropy and Knowledge base. Chris Cornelis has researched Artificial intelligence in several fields, including Mathematical economics, Machine learning and Pattern recognition.
Chris Cornelis mainly focuses on Artificial intelligence, Fuzzy logic, Rough set, Fuzzy set and Data mining. The study incorporates disciplines such as Natural language processing, Machine learning and Pattern recognition in addition to Artificial intelligence. His biological study spans a wide range of topics, including Discrete mathematics, Contrast and Pure mathematics.
His research in Fuzzy set intersects with topics in Representation, Theoretical computer science and Fuzzy control system. As a member of one scientific family, Chris Cornelis mostly works in the field of Data mining, focusing on k-nearest neighbors algorithm and, on occasion, Reduction. His Fuzzy set operations study incorporates themes from Fuzzy number, Defuzzification and Fuzzy classification.
Chris Cornelis focuses on Artificial intelligence, Rough set, Data mining, Fuzzy logic and Machine learning. His Artificial intelligence study frequently draws connections to other fields, such as Pattern recognition. The Rough set study combines topics in areas such as Discrete mathematics, Fuzzy set, Approximation operators and Algorithm.
Chris Cornelis works in the field of Fuzzy set, namely Fuzzy set operations. His work carried out in the field of Fuzzy set operations brings together such families of science as Fuzzy number, Fuzzy classification, Robustness and Complete information. Chris Cornelis interconnects Representation, Weighted arithmetic mean and Extension in the investigation of issues within Fuzzy logic.
Chris Cornelis mostly deals with Artificial intelligence, Rough set, Data mining, Dominance-based rough set approach and Fuzzy set operations. His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition. His Rough set research incorporates elements of Discrete mathematics, Spectral theorem, Partition and Decision rule.
His Data mining research integrates issues from Class and Fuzzy logic. His Dominance-based rough set approach research incorporates themes from Approximations of π, Theoretical computer science, Boolean algebra and Complete information. His research integrates issues of Fuzzy number and Fuzzy classification in his study of Fuzzy set operations.
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On the representation of intuitionistic fuzzy t-norms and t-conorms
G. Deschrijver;C. Cornelis;E.E. Kerre.
IEEE Transactions on Fuzzy Systems (2004)
Implication in intuitionistic fuzzy and interval-valued fuzzy set theory: construction, classification, application
Chris Cornelis;Glad Deschrijver;Etienne E. Kerre.
International Journal of Approximate Reasoning (2004)
Rough Sets and Intelligent Systems Paradigms
M. Kryszkiewicz;C. Cornelis;D. Ciucci;J. Medina Moreno.
Lecture Notes in Computer Science (2007)
Attribute selection with fuzzy decision reducts
Chris Cornelis;Richard Jensen;Germán Hurtado;Dominik lezak.
Information Sciences (2010)
Advances and challenges in interval-valued fuzzy logic
C. Cornelis;G. Deschrijver;E. E. Kerre.
Fuzzy Sets and Systems (2006)
Intuitionistic fuzzy rough sets: at the crossroads of imperfect knowledge
Chris Cornelis;Martine De Cock;Etienne E. Kerre.
Expert Systems (2003)
Gradual trust and distrust in recommender systems
Patricia Victor;Chris Cornelis;Martine De Cock;Paulo Pinheiro da Silva.
international workshop on fuzzy logic and applications (2009)
Trust- and Distrust-Based Recommendations for Controversial Reviews
P Victor;C Cornelis;M D Cock;A M Teredesai.
web science (2009)
Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”
Lala Septem Riza;Andrzej Janusz;Christoph Bergmeir;Chris Cornelis.
Information Sciences (2014)
Trust and Recommendations
Patricia Victor;Martine De Cock;Chris Cornelis.
Recommender systems handbook (2011)
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