Robust Rank Correlation Coefficients on the Basis of Fuzzy.
Rough membership functions: a tool for reasoning with uncertainty
A variety of numerical approaches for reasoning with uncertainty have been investigated in the literature. We propose rough membership functions, rm-functions for short, as a basis for such reasoning. These functions have values in the interval [0,1] and are computable on the basis of the observable information about the objects rather than on the objects themselves. We investigate properties of the rm-functions. In particular, we show that our approach is intensional with respect to the class of...
Rough modeling - a bottom-up approach to model construction
Traditional data mining methods based on rough set theory focus on extracting models which are good at classifying unseen obj-ects. If one wants to uncover new knowledge from the data, the model must have a high descriptive quality-it must describe the data set in a clear and concise manner, without sacrificing classification performance. Rough modeling, introduced by Kowalczyk (1998), is an approach which aims at providing models with good predictive emphand descriptive qualities, in addition to...
Rough relation properties
Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. This paper rewrites some properties of rough relations found in the literature, proving their validity.
Rough set-based dimensionality reduction for supervised and unsupervised learning
The curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques. Widely approved and otherwise elegant methodologies used for a number of different tasks ranging from classification to function approximation exhibit relatively high computational complexity with respect to dimensionality. This limits severely the applicability of such techniques to real world problems. Rough set theory is a formal methodology that can be employed to reduce the dimensionality...
Rough sets methods in feature reduction and classification
The paper presents an application of rough sets and statistical methods to feature reduction and pattern recognition. The presented description of rough sets theory emphasizes the role of rough sets reducts in feature selection and data reduction in pattern recognition. The overview of methods of feature selection emphasizes feature selection criteria, including rough set-based methods. The paper also contains a description of the algorithm for feature selection and reduction based on the rough...
Rule-based fuzzy object similarity.
A new similarity measure for objects that are represented by feature vectors of fixed dimension is introduced. It can simultaneously deal with numeric and symbolic features. Also, it can tolerate missing feature values. The similarity measure between two objects is described in terms of the similarity of their features. IF-THEN rules are being used to model the individual contribution of each feature to the global similarity measure between a pair of objects. The proposed similarity measure is based...
Satisfiability and matchings in bipartite graphs: relationship and tractability.
Satisfiability problem is the task to establish either a given CNF logical formula admits a model or not. It is known to be the canonical NP-complete problem. We study in this note the relationship between matchings in bipartite graphs and the satisfiability problem, and prove that some restrictions of formulae including the known r-r-SAT1 class are trivially satisfiable. We present an algorithm which finds a model for such formulas in polynomial time complexity if one exists or, failing this, proves...
Scaling of model approximation errors and expected entropy distances
We compute the expected value of the Kullback-Leibler divergence of various fundamental statistical models with respect to Dirichlet priors. For the uniform prior, the expected divergence of any model containing the uniform distribution is bounded by a constant . For the models that we consider this bound is approached as the cardinality of the sample space tends to infinity, if the model dimension remains relatively small. For Dirichlet priors with reasonable concentration parameters the expected...
Scheduling trajectories on a planar surface with moving obstacles.
Schema mapping and query reformulation in peer-to-peer XML data integration system
Secuenciación dinámica de sistemas de fabricación flexible mediante aprendizaje automático: análisis de los principales sistemas de secuenciación existentes.
Una forma habitual de secuenciar de modo dinámico los trabajos en los sistemas de fabricación es mediante el empleo de reglas de secuenciación. Sin embargo, el problema que presenta este método es que el comportamiento del sistema de fabricación dependerá de su estado, y no existe una regla que supere a las demás en todos los posibles estados que puede presentar el sistema de fabricación. Por lo tanto, sería interesante usar en cada momento la regla más adecuada. Para lograr este objetivo, se pueden...
Selection of search strategies for solving 3-sat problems
Self-adaptation of parameters in a learning classifier system ensemble machine
Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem...
Self-learning facial emotional feature selection based on rough set theory.
Semantic analysis of topic and focus
Semantics of MML Query
In the paper the semantics of MML Query queries is given. The formalization is done according to [4]
Semantics of MML Query - Ordering
Semantics of order directives of MML Query is presented. The formalization is done according to [1]
Semi-topological classification of line patterns in the plane
Sequent Calculus, Derivability, Provability. Gödel's Completeness Theorem
Fifth of a series of articles laying down the bases for classical first order model theory. This paper presents multiple themes: first it introduces sequents, rules and sets of rules for a first order language L as L-dependent types. Then defines derivability and provability according to a set of rules, and gives several technical lemmas binding all those concepts. Following that, it introduces a fixed set D of derivation rules, and proceeds to convert them to Mizar functorial cluster registrations...