Browsing Advanced Reasoning Group by Title

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  • MacParthaláin, Neil Seosamh; Jensen, Richard; Shen, Qiang (2008-06-01)
    Dataset dimensionality is undoubtedly the single most significant obstacle which exasperates any attempt to apply effective computational intelligence techniques to problem domains. In order to address this problem a ...
  • Shen, Qiang; Tuson, Andrew; Jensen, Richard (2005)
    Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. ...
  • Shen, Qiang; Daly, Ronan (2005)
    A method is proposed, whereby a particular application of an operator, applied to a structure representing a Bayesian network equivalence class can be scored in a generic fashion. This is achieved by representing a particular ...
  • Galea, Michelle; Shen, Qiang (2004)
    An approach to fuzzy rule induction inspired by the foraging behaviour of ants is presented. The implemented system - FRANTIC - is tested on a real classification problem against two other fuzzy rule induction algorithms, ...
  • He, Jun; Yao, Xin (2002-10)
    Almost all analyses of time complexity of evolutionary algorithms (EAs) have been conducted for (1 + 1) EAs only. Theoretical results on the average computation time of population-based EAs are few. However, the vast ...
  • Bell, Jonathan; Price, Chris; Snooke, Neal (2005-05)
    Description of system function is already in use as the basis of an approach to interpretation of the results of simulation in design analysis, allowing an automated design analysis tool to generate a textual report detailing ...
  • Fu, X.; Shen, Q. (2011-07)
    There are a variety of measures to describe classification performance with respect to different criteria and they are often represented by numerical values. Psychologists have commented that human beings can only reasonably ...
  • Fu, Xin; Shen, Qiang (2010-08)
    Automated modeling refers to automatic (re-)formulation of alternative system models that embody the simplification, abstraction, and approximation of knowledge and data for a given task. This technique is highly desirable ...
  • Shen, Qiang; Yang, M. (2008)
    This paper presents a fuzzy knowledge-based system for turbomachinery diagnosis. Given symptoms associated with a vibration problem, the system can identify and rank possible causes by performing incremental forward chaining. ...
  • MacParthaláin, Neil Seosamh; Jensen, Richard; Shen, Qiang (2006-07-26)
    Feature Selection (FS) is a dimensionality reduction technique that aims to select a subset of the original features of a dataset which offer the most useful information. The benefits of feature selection include improved ...
  • Huang, Zhiheng; Shen, Qiang (2008-02-26)
    Fuzzy interpolation does not only help to reduce the complexity of fuzzy models, but also makes inference in sparse rule-based systems possible. It has been successfully applied to systems control, but limited work exists ...
  • Shen, Qiang; Huang, Zhiheng (2004)
    Fuzzy interpolative reasoning offers the potential to model problems using sparse rule bases, as opposed to dense rule bases deployed in traditional fuzzy systems. It thus supports the simplification of complex fuzzy models ...
  • Shen, Qiang; Huang, Zhiheng (2006-04)
    Interpolative reasoning does not only help reduce the complexity of fuzzy models but also makes inference in sparse rule-based systems possible. This paper presents an interpolative reasoning method by means of scale and ...
  • Shen, Qiang; Fu, Xin (2008-06-01)
    Given a set of collected evidence and a knowledge base, Fuzzy Compositional Modelling (FCM) begins by retrieving model fragments which are the most likely to be relevant to the available data. Since FCM often involves ...
  • Shen, Qiang; Boongoen, Tossapon; Price, Chris (Qualitative Reasoning, 2011-09-27)
    Numerical link-based similarity techniques have proven effective for identifying similar objects in the Internet and publication domains. However, for cases involving unduly high similarity measures, these methods usually ...
  • Boongoen, Tossapon; Shen, Qiang; Price, Christopher John (2011-06)
    Many approaches have been developed for academic performance evaluation using various fuzzy techniques. Initial methods rely greatly on experts' specification of analytical parameters, without making use of valuable ...
  • Verbiest, Nele; Cornelis, Chris; Jensen, Richard (IEEE, 2012)
    This paper proposes a classifier that uses fuzzy rough set theory to improve the Fuzzy Nearest Neighbour (FNN) classifier. We show that previous attempts to use fuzzy rough set theory to improve the FNN algorithm have some ...
  • Vander Heyden, Yvan; Dejaegher, Bieke; Jensen, Richard; Funar-Timofei, Simona; Goodarzi, Mohammad (2011-07-13)
    In cancer chemotherapy, multidrug resistance (MDR) is a major clinical problem which occurs by an influential mechanism and which leads to the failure of cancer chemotherapy and/or a relapse of the cancer. In this study, ...
  • Shen, Qiang; Galea, Michelle (2004)
    A new approach to fuzzy rule induction from historical data is presented. The implemented system - FRANTIC - is a tested on a simple classification problem against a fuzzy tree induction algorithm, a genetic algorithm, and ...
  • Shen, Qiang (Springer Berlin Heidelberg, 2009)
    Both fuzzy set theory and rough set theory play an important role in data-driven, systems modelling and analysis. They have been successfully applied to building various intelligent decision support systems (amongst many ...

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