Advances on Computational Intelligence: 14th International by Carlos Cernuda, Edwin Lughofer, Lisbeth Suppan, Thomas

By Carlos Cernuda, Edwin Lughofer, Lisbeth Suppan, Thomas Röder, Roman Schmuck (auth.), Salvatore Greco, Bernadette Bouchon-Meunier, Giulianella Coletti, Mario Fedrizzi, Benedetto Matarazzo, Ronald R. Yager (eds.)

These 4 volumes (CCIS 297, 298, 299, three hundred) represent the complaints of the 14th overseas convention on info Processing and administration of Uncertainty in Knowledge-Based platforms, IPMU 2012, held in Catania, Italy, in July 2012. The 258 revised complete papers provided including six invited talks have been conscientiously reviewed and chosen from quite a few submissions. The papers are equipped in topical sections on fuzzy laptop studying and online modeling; computing with phrases and determination making; gentle computing in computing device imaginative and prescient; tough units and intricate information research: idea and purposes; clever databases and knowledge procedure; details fusion structures; philosophical and methodological elements of soppy computing; uncomplicated concerns in tough units; fortieth anniversary of the measures of fuziness; SPS11 uncertainty in profiling platforms and purposes; dealing with uncertainty with copulas; formal the way to take care of uncertainty of many-valued occasions; linguistic summarization and outline of information; fuzzy implications: thought and purposes; sensing and knowledge mining for instructing and studying; idea and purposes of intuitionistic fuzzy units; approximate features of information mining and database analytics; fuzzy numbers and their functions; info processing and administration of uncertainty in knowledge-based platforms; aggregation services; vague percentages; probabilistic graphical types with imprecision: conception and purposes; trust functionality conception: fundamentals and/or functions; fuzzy uncertainty in economics and company; new developments in De Finetti's strategy; fuzzy measures and integrals; multicriteria choice making; uncertainty in privateness and defense; uncertainty within the spirit of Pietro Benvenuti; coopetition; online game thought; probabilistic approach.

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Extra resources for Advances on Computational Intelligence: 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Catania, Italy, July 9-13, 2012. Proceedings, Part I

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5 Conclusions and Perspectives This article deals with the hypothetical and theoretical blockage of the cooling system of assemblies in the experimental nuclear reactor Phenix. The dataset is the output temperature map of the reactor, it is provided by the Atomic Statistical Dynamic Classification to Detect Changes 19 Energy and Alternative Energies Commission (CEA). The early detection of an assembly blockage is depending on the variation of temperatures of its two neighbours. However in steady state, some fluctuations of these temperatures are quite important, so that it makes complex the processing of these data.

Classifying uncovered examples by rule stretching. In: Proceedings of the ICILP 2001, pp. 41–50 (2001) 6. : Prie: a system for generating rule lists to maximize roc performance. Data Mining and Knowledge Discovery 17, 207–224 (2008) 7. : Generating accurate rule sets without global optimization. In: Proc. of Int. Conf. in Mach. Learning, pp. 144–151 (1998) A Possibilistic Rule-Based Classifier 31 8. : Separate-and-conquer rule learning. Art. Intel. Review 13, 3–54 (1999) 9. : Furia: An algorithm for unordered fuzzy rule induction.

In particular, the PRCs learn fuzzy rules instead of crisp rules, consider weighted rules at deduction time in an unordered manner instead of rule lists. They also reduce the number of examples not covered by any rule, using a fuzzy rule set with large supports. The experiments reported show that the PRCs lead to improve the accuracy of the classical PART algorithm. Keywords: possibilistic rule-based classifier, fuzzy rules, decision list. 1 Introduction Rule induction algorithms are currently used in machine learning applications.

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