Browsing Advanced Reasoning Group by Title

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  • Chen, Tianshi; He, Jun; Chen, Guoliang; Yao, Xin (2009-10-01)
    In the past decades, many theoretical results related to the time complexity of evolutionary algorithms (EAs) on different problems are obtained. However, there is not any general and easy-to-apply approach designed ...
  • Jensen, Richard; Cornelis, Chris (2008-10-17)
    In this paper, we present a new fuzzy-rough nearest neighbour (FRNN) classification algorithm, as an alternative to Sarkar’s fuzzy-rough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the ...
  • Jensen, Richard; Cornelis, Chris (2011)
    A new fuzzy-rough nearest neighbour (FRNN) classification algorithm is presented in this paper, as an alternative to Sarkar’s fuzzy-rough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the ...
  • Jensen, Richard; Shen, Qiang (2009-08)
    There has been great interest in developing methodologies that are capable of dealing with imprecision and uncertainty. The large amount of research currently being carried out in fuzzy and rough sets is representative of ...
  • Jensen, Richard; Cornelis, Chris (2008)
    In rough set based feature selection, the goal is to omit attributes (features) from decision systems such that objects in different decision classes can still be discerned. A popular way to evaluate attribute subsets with ...
  • Fu, Xin; Shen, Qiang (2009-08)
    Dealing with various inexact pieces of information has become an intrinsically important issue in knowledge-based reasoning, because many problem domains involve imprecise, incomplete and uncertain information. Indeed, ...
  • Lee, Mark (1999)
    What are models and how is the relatively new technology of Model-Based Reasoning different from conventional modelling in science and engineering? These questions are explored in this paper by examining the fundamental ...
  • MacParthaláin, Neil Seosamh; Shen, Qiang (2010-12)
    Rough set theory (RST) has enjoyed an enormous amount of attention in recent years and has been applied to many real-world problems including data mining, pattern recognition, and intelligent control. Much research has ...
  • King, Ross Donald; Garrett, Simon; Coghill, George (Oxford Journals, 2005-01)
    Motivation: Perhaps the greatest challenge of modern biology is to develop accurate in silico models of cells. To do this we require computational formalisms for both simulation (how according to the model the state of the ...
  • Shen, Qiang; Boongoen, Tossapon (2009)
    Combating identity fraud is crucial and urgent as false identity has become the common denominator of all serious crime, including mafia trafficking and terrorism. Typical approaches to detecting the use of false identity ...
  • Jensen, Richard; Verbiest, Nele; Cornelis, Chris (2010-10)
    Traditionally, membership to the fuzzy-rough lower, resp. upper approximation is determined by looking only at the worst, resp. best performing object. Consequently, when applied to data analysis problems, these approximations ...
  • Jensen, Richard (Springer Verlag, 2006)
    The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In real world problems FS is a must due to ...
  • Currie, David J.; Lee, Mark; Todd, R. W. (2006)
    This work concerns the modelling of the dielectric properties of biological cell suspensions. The relative permittivity of a suspension is proportional to the volume fraction of suspended cells but it would be more useful ...
  • Shen, Qiang; Huang, Zhiheng (2005)
    Fuzzy interpolative reasoning serves as an important role in fuzzy modelling as it does not only help reduce rule number but also provides an inference mechanism for sparse rule bases. The preservation of piece-wise linearity ...
  • Shen, Qiang; Huang, Zhiheng (IEEE, 2009-08)
    Fuzzy interpolative reasoning plays an important role in fuzzy modelling as it not only helps to reduce the number of rules in a rule base, but also provides an inference mechanism for sparse rule bases. In interpolation, ...
  • Keppens, Jeroen; Schafer, Burkhard; Shen, Qiang (2005)
    This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for crime investigation: symbolic crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. ...
  • Eckerdal, Anna; Ratcliffe, Mark; Sanders, Kate; Moström, Jan Erik; Zander, Carol; McCartney, Robert (2006)
    This paper describes Threshold Concepts, a theory of learning that distinguishes core concepts whose characteristics can make them troublesome in learning. With an eye to applying this theory in computer science, we consider ...
  • Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan (2012-12-01)
    A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous ...
  • Lee, Mark (1999-07)
    The engineering task of failure analysis involves reasoning about the behaviour of a system using appropriate models of system components and structure. This paper describes methods of qualitatively modelling electrical ...
  • Price, Chris; Trave-Massuyes, Louise; Milne, Rob; Ironi, Liliana; Forbus, Kenneth; Bredeweg, Bert; Lee, Mark; Struss, Peter; Snooke, Neal; Lucas, Peter; Cavazza, Marc; Coghill, George (2006-12-04)
    This paper reviews the state of the art in model-based systems and qualitative reasoning, and considers where the field will be in twenty years time. It highlights six areas where developments in model-based systems in ...

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