Browsing Advanced Reasoning Group by Title

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  • Shen, Qiang; Boongoen, Tossapon (2009-08)
    Within the past decades, many fuzzy aggregationtechniques, ordered weighted averaging (OWA) in particular, have proven effective for a wide range of information processing tasks, such as decision making, image analysis, ...
  • Snooke, Neal (1999-01-01)
    Automotive electrical and electronic systems have become very sophisticated in a relatively short time. This complexity has both led to the increased need for design analysis tools and the need for these tools to deal with ...
  • Galea, Michelle; Shen, Qiang (Springer, 2006)
    An approach based on Ant Colony Optimisation for the induction of fuzzy rules is presented. Several Ant Colony Optimisation algorithms are run simultaneously, with each focusing on finding descriptive rules for a specific ...
  • Daly, Ronan; Aitken, Stuart; Shen, Qiang (2006)
    For some time, learning Bayesian networks has been both feasible and useful in many problems domains. Recently research has been done on learning equivalence classes of Bayesian networks, i.e. structures that capture all ...
  • Theile, Madeleine; Jansen, Thomas (2010-05)
    The modeling and analysis of large networks of autonomous agents is an important topic with applications in many different disciplines. One way of modeling the development of such networks is by means of an evolutionary ...
  • Rasmani, Khairul; Shen, Qiang (2004)
    Reasoning with fuzzy rule-based models has been widely applied to perform various real world classification tasks. The main advantage of this approach is that it supports inferences in the way people think and make judgements. ...
  • Rasmani, Khairul; Shen, Qiang (2005)
    The focus of this paper is the use of fuzzy approaches to classify student academic performance, which so far has not been performed satisfactorily by existing fuzzy techniques. Instead of using methods that solely rely ...
  • Lingras, Pawan; Jensen, Richard (2007)
    This paper provides a broad overview of logical and black box approaches to fuzzy and rough hybridization. The logical approaches include theoretical, supervised learning, feature selection, and unsupervised learning. The ...
  • Shen, Qiang; Keppens, Jeroen; Schafer, Burkhard (Hart Publishing, 2007-11)
  • Jensen, Richard; Shen, Qiang (2007)
    One of the main obstacles facing the application of computational intelligence technologies in pattern recognition (and indeed in many other tasks) is that of dataset dimensionality. To enable pattern classifiers to be ...
  • Yang, Longzhi; Shen, Qiang (2009-08)
    Fuzzy interpolative reasoning has been extensively studied due to its ability to enhance the robustness of fuzzy systems and to reduce system complexity. However, during the interpolation process, it is possible that ...
  • He, Jun; Yao, Xin (2003-04)
    In spite of many applications of evolutionary algorithms in optimisation, theoretical results on the computation time and time complexity of evolutionary algorithms on different optimisation problems are relatively few. ...
  • Jin, Shangzhu; Diao, Ren; Shen, Qiang (School of Computer Science, University of Manchester, 2011-09-26)
    Fuzzy rule interpolation (FRI) is well known forreducing the complexity of fuzzy models and making inferencepossible in sparse rule-based systems. However, in practicalfuzzy applications with inter-connected rule bases, ...
  • Shen, Qiang; Zhao, Ruiqing; Fu, Xin (2007-07-23)
    Compositional Modelling (CM) has been applied to synthesize automatically plausible scenarios in many problem domains with promising results. However, it is assumed that the generic and reusable model fragments within the ...
  • Chen, Chengyuan; Shen, Qiang (School of Computer Science, University of Manchester, 2011-09-07)
    Fuzzy rule interpolation is an important technique for performing inferences with sparse rule bases. Even when given observations have no overlap with the antecedent values of any rule, fuzzy rule interpolation may still ...
  • Shen, Qiang; Huang, Zhiheng (2005)
    Fuzzy interpolation offers the potential to model problems with sparse rule bases, as opposed to dense rule bases deployed in traditional fuzzy systems. It thus supports the simplification of complex fuzzy models and ...
  • Diao, Ren; Shen, Qiang (2010-07)
    Many search strategies have been exploited in implementing feature selection, in an effort to identify smaller and better subsets. Such work typically involves the use of heuristics in one form or another. In this paper ...
  • Shen, Qiang; Daly, Ronan; Aitken, Stuart (2006)
    Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm to learn the structure of a Bayesian network. It does this by conducting a search through the space of ...
  • Jensen, Richard; Shen, Qiang (2006)
    Due to the explosive growth of electronically stored information, automatic methods must be developed to aid users in maintaining and using this abundance of information effectively. In particular, the sheer volume of ...

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