Browsing Advanced Reasoning Group by Title

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Browsing Advanced Reasoning Group by Title

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  • MacParthaláin, Neil Seosamh; Jensen, Richard (2010)
    For supervised learning, feature selection algorithms attemptto maximise a given function of predictive accuracy.This function usually considers the ability of feature vectorsto reflect decision class labels. It is therefore ...
  • Shen, Qiang; Daly, Ronan (2007)
    Bayesian networks have become a standard technique in the representation of uncertain knowledge. This paper proposes methods that can accelerate the learning of a Bayesian network structure from a data set. These methods ...
  • Liang, Shen; He, Jun (2010-07)
    The performance of Evolutionary Programming (EP) is affected by many factors (e.g. mutation operators and selection strategies). Although the conventional approach with Gaussian mutation operator may be efficient, the ...
  • He, Jun; Kang, Lishan (2009-04-23)
    Multigrid methods have been proven to be an efficient approach in accelerating the convergence rate of numerical algorithms for solving partial differential equations. This paper investigates whether multigrid methods are ...
  • Snooke, Neal (2004-06)
    Failure Mode and Effects Analysis is widely used in engineering hardware systems to help in understanding the effects of potential failures and the faults that cause them to occur. The analysis is iterative leading to ...
  • Lee, Mark (2000)
    The software engineering industry suffers from almost unmanageable complexity both in the products it produces and in the processes of production. One of the current shortcomings in the software production process is the ...
  • Price, Chris; Struss, Peter (2004-01)
    The automotive industry was the first to promote the development of applications of model-based systems technology on a broad scale and, as a result, has produced some of the most advanced prototypes and products. In this ...
  • Shen, Qiang; Zhao, Ruiqing; Tang, Wansheng (2008-10)
    This short paper discusses the modeling of random fuzzy renewal reward processes in which the interarrival times and rewards are represented by nonnegative random fuzzy variables. Based on random fuzzy theory, a random ...
  • Shen, Qiang; Rasmani, Khairul (2004)
    The use of fuzzy quantifiers in linguistic fuzzy models helps to build fuzzy systems that use linguistic terms in a more natural way. Although several fuzzy quantification techniques have been developed, the application ...
  • Doerr, Benjamin; Jansen, Thomas; Sudholt, Dirk; Winzen, Carola; Zarges, Christine (2013)
    Extending previous analyses on function classes like linear functions, we analyze how the simple (1+1) evolutionary algorithm optimizes pseudo-Boolean functions that are strictly monotonic. These functions have the property ...
  • Boongoen, Tossapon; Shen, Qiang (2010-12)
    The intuition of data reliability has recently been incorporated into the main stream of research on ordered weighted averaging (OWA) operators. Instead of relying on human-guided variables, the aggregation behavior is ...
  • 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 ...
  • 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 (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; 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 (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 ...

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