Novel Developments in Uncertainty Representation and Processing

Opposition-based models are a current hot-topic in knowledge representation. The point of this paper is to suggest that opposition can be in fact introduced at two different levels, those of the predicates of interest being represented (as short/tall) and of the logical references (true/false) used to evaluate the verification of the former. We study this issue by means of the consideration of different paired structures at each level. We also pay attention at how different types of fuzziness may be introduced in these paired structures to model imprecision and lack of knowledge. As a consequence, we obtain a unifying framework for studying the relationships between different knowledge representation models and different kinds of uncertainty.

[1]  Fevrier Valdez,et al.  A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias , 2014, Inf. Sci..

[2]  Krassimir T. Atanassov,et al.  Intercriteria Decision Making Approach to EU Member States Competitiveness Analysis , 2014, BMSD 2014.

[3]  Vassia Atanassova.  Interpretation in the Intuitionistic Fuzzy Triangle of the Results, Obtained by the InterCriteria Analysis , 2015, IFSA-EUSFLAT.

[4]  Krassimir T. Atanassov,et al.  On four intuitionistic fuzzy topological operators , 2001 .

[5]  Debashis Chatterjee,et al.  An application of PSO technique for harmonic elimination in a PWM inverter , 2009, Appl. Soft Comput..

[6]  Yasser M. Kadah,et al.  Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification , 2002, IEEE Transactions on Biomedical Engineering.

[7]  Krassimir T. Atanassov,et al.  Index Matrices: Towards an Augmented Matrix Calculus , 2014, Studies in Computational Intelligence.

[8]  Dimitar Karastoyanov,et al.  InterCriteria Decision Making Approach for Iron Powder Briquetting , 2015, BMSD 2015.

[9]  João Miguel da Costa Sousa,et al.  Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients , 2013, Appl. Soft Comput..

[10]  Hadi Sadoghi Yazdi,et al.  ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm , 2009, 2009 Third UKSim European Symposium on Computer Modeling and Simulation.

[11]  Mounir Boukadoum,et al.  Improved global-best particle swarm optimization algorithm with mixed-attribute data classification capability , 2014, Appl. Soft Comput..

[12]  Carlos Eduardo Pedreira,et al.  Learning vector quantization with training data selection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Krassimir T. Atanassov,et al.  On Intuitionistic Fuzzy Sets Theory , 2012, Studies in Fuzziness and Soft Computing.

[14]  Dimitrios I. Fotiadis,et al.  An arrhythmia classification system based on the RR-interval signal , 2005, Artif. Intell. Medicine.

[15]  Robert Sabourin,et al.  Fast intelligent watermarking of heterogeneous image streams through mixture modeling of PSO populations , 2013, Appl. Soft Comput..

[16]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[17]  José S. Torrecilla,et al.  Self-organizing maps and learning vector quantization networks as tools to identify vegetable oils and detect adulterations of extra virgin olive oil , 2010 .