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Calculations of graded ill-known sets

Masahiro Inuiguchi (2014)

Kybernetika

To represent a set whose members are known partially, the graded ill-known set is proposed. In this paper, we investigate calculations of function values of graded ill-known sets. Because a graded ill-known set is characterized by a possibility distribution in the power set, the calculations of function values of graded ill-known sets are based on the extension principle but generally complex. To reduce the complexity, lower and upper approximations of a given graded ill-known set are used at the...

Can interestingness measures be usefully visualized?

Robert Susmaga, Izabela Szczech (2015)

International Journal of Applied Mathematics and Computer Science

The paper presents visualization techniques for interestingness measures. The process of measure visualization provides useful insights into different domain areas of the visualized measures and thus effectively assists their comprehension and selection for different knowledge discovery tasks. Assuming a common domain form of the visualized measures, a set of contingency tables, which consists of all possible tables having the same total number of observations, is constructed. These originally four-dimensional...

Capital budgeting problems with fuzzy cash flows.

Christer Carlsson, Robert Fuller (1999)

Mathware and Soft Computing

We consider the internal rate of return (IRR) decision rule in capital budgeting problems with fuzzy cash flows. The possibility distribution of the IRR at any r ≥ 0, is defined to be the degree of possibility that the (fuzzy) net present value of the project with discount factor r equals to zero. Generalizing our earlier results on fuzzy capital budegeting problems [Car99] we show that the possibility distribution of the {IRR} is a highly nonlinear function which is getting more and more unbalanced...

Cascading classifiers

Ethem Alpaydin, Cenk Kaynak (1998)

Kybernetika

We propose a multistage recognition method built as a cascade of a linear parametric model and a k -nearest neighbor ( k -NN) nonparametric classifier. The linear model learns a “rule” and the k -NN learns the “exceptions” rejected by the “rule.” Because the rule-learner handles a large percentage of the examples using a simple and general rule, only a small subset of the training set is stored as exceptions during training. Similarly during testing, most patterns are handled by the rule -learner and...

Cayley's Theorem

Artur Korniłowicz (2011)

Formalized Mathematics

The article formalizes the Cayley's theorem saying that every group G is isomorphic to a subgroup of the symmetric group on G.

Classification results in quasigroup and loop theory via a combination of automated reasoning tools

Volker Sorge, Simon Colton, Roy McCasland, Andreas Meier (2008)

Commentationes Mathematicae Universitatis Carolinae

We present some novel classification results in quasigroup and loop theory. For quasigroups up to size 5 and loops up to size 7, we describe a unique property which determines the isomorphism (and in the case of loops, the isotopism) class for any example. These invariant properties were generated using a variety of automated techniques --- including machine learning and computer algebra --- which we present here. Moreover, each result has been automatically verified, again using a variety of techniques...

Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems

Jecheva, Veselina, Nikolova, Evgeniya (2009)

Serdica Journal of Computing

Intrusion detection is a critical component of security information systems. The intrusion detection process attempts to detect malicious attacks by examining various data collected during processes on the protected system. This paper examines the anomaly-based intrusion detection based on sequences of system calls. The point is to construct a model that describes normal or acceptable system activity using the classification trees approach. The created database is utilized as a basis for distinguishing...

Clustering of vaguely defined objects

Libor Žák (2003)

Archivum Mathematicum

This paper is concerned with the clustering of objects whose properties cannot be described by exact data. These can only be described by fuzzy sets or by linguistic values of previously defined linguistic variables. To cluster these objects we use a generalization of classic clustering methods in which instead of similarity (dissimilarity) of objects, used fuzzy similarity (fuzzy dissimilarity) to define the clustering of fuzzy objects.

Cocktail: a tool for deriving correct programs.

Michael Franssen, Harrie De Swart (2004)

RACSAM

Cocktail is a tool for deriving correct programs from their specifications. The present version is powerful enough for educational purposes. The tool yields support for many sorted first order predicate logic, formulated in a pure type system with parametric constants (CPTS), as the specification language, a simple While-language, a Hoare logic represented in the same CPTS for deriving programs from their specifications and a simple tableau based automated theorem prover for verifying proof obligations....

Combination of mobile agent and evolutionary algorithm to optimize the client transport services

Hayfa Zgaya, Slim Hammadi, Khaled Ghédira (2008)

RAIRO - Operations Research

This paper presents a migration strategy for a set of mobile agents (MAs) in order to satisfy customers' requests in a transport network, through a multimodal information system. In this context, we propose an optimization solution which operates on two levels. The first one aims to constitute a set of MAs building their routes, called Workplans. At this level, Workplans must incorporate all nodes, representing information providers in the multimodal network, in order to explore it completely....

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