Browsing Cyfrifiadureg / Computer Science by Title

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Browsing Cyfrifiadureg / Computer Science by Title

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  • Fuzzy qualitative link analysis for academic performance evaluation 
    Price, Chris; Shen, Qiang; Boongoen, Tossapon (2011)
    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 ...
  • Fuzzy qualitative trigonometry 
    Liu, Honghai; Coghill, George; Barnes, David Preston (2009-09-06)
    This paper presents a fuzzy qualitative representation of conventional trigonometry with the goal of bridging the gap between symbolic cognitive functions and numerical sensing & control tasks in the domain of physical ...
  • Fuzzy-rough approaches for mammographic risk analysis 
    MacParthaláin, N.; Jensen, R.; Shen, Q.; Zwiggelaar, R. (2010-03-15)
    The accuracy of methods for the assessment of mammographic risk analysis is heavily related to breast tissue characteristics. Previous work has demonstrated considerable success in developing an automatic breast tissue ...
  • Fuzzy-Rough Attribute Reduction with Application to Web Categorization. 
    Jensen, Richard; Shen, Qiang (2004)
    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 ...
  • Fuzzy-rough Classifier Ensemble Selection 
    Diao, Ren; Shen, Qiang (0, 2011-09-06)
    Classifier ensembles constitute one of the main research directions in machine learning and data mining. Ensembles allow higher accuracy to be achieved which is otherwise often not achievable with a single classifier. A ...
  • Fuzzy-rough data mining 
    Jensen, Richard (2011-12-31)
  • Fuzzy-Rough Data Reduction with Ant Colony Optimization. 
    Shen, Qiang; 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. ...
  • Fuzzy-Rough Feature Significance for Fuzzy Decision Trees 
    Jensen, Richard; Shen, Qiang (2005)
    Crisp decision trees are one of the most popular classification algorithms in current use within data mining and machine learning. However, although they possess many desirable features, they lack the ability to model ...
  • Fuzzy-rough instance selection 
    Jensen, Richard; Cornelis, Chris (2010)
    Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Since its introduction, this theory has been successfully ...
  • Fuzzy-Rough Nearest Neighbour Classification 
    Jensen, Richard; Cornelis, Chris (2011-12-31)
  • Fuzzy-Rough Nearest Neighbour Classification and Prediction 
    Cornelis, Chris; Jensen, Richard (2011-09-07)
    Nearest neighbour (NN) approaches are inspired by the way humans make decisions, comparing a test object to previously encountered samples. In this paper, we propose an NN algorithm that uses the lower and upper approximations ...
  • Fuzzy Rough Positive Region based Nearest Neighbour Classification 
    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 ...
  • Fuzzy rough set as feature selection for QSAR modeling of 2,4,5-trisubstituted imidazoles, nontoxic modulators of P-glycoprotein mediated multidrug resistance 
    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, ...
  • Fuzzy-Rough Set based Semi-Supervised Learning 
    MacParthalain, Neil; Jensen, Richard (2011-07-06)
    Much work has been carried out in the area of fuzzy-rough sets for supervised learning. However, very little has been accomplished for the unsupervised or semi-supervised tasks. For many real-word applications, it is often ...
  • Fuzzy-Rough Sets Assisted Attribute Selection 
    Shen, Qiang; Jensen, Richard (2007)
    Attribute selection (AS) refers to the problem of selecting those input attributes or features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition ...
  • Fuzzy rules from ant-inspired computation 
    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 ...
  • Fuzzy Sets and Rough Sets for Scenario Modelling and Analysis 
    Shen, Qiang (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 ...
  • A fuzzy-XCS classifier system with linguistic hedges 
    Marin-Blázquez, Javier; Shen, Qiang (2008)
    Many real-world problems require the development and application of algorithms that automatically generate human interpretable knowledge from historical data. Most existing algorithms for rule induction from imprecise data ...
  • Gaze modulated visual search integrating spatial and feature data : Embodied visual memory for robotic systems II 
    Hülse, Martin; McBride, Sebastian; Lee, Mark (2010-11)
    Substantial evidence supports the role of lateral intraparietal region (LIP) of the brain as the central processing point where bottom-up visual information is modulated by top-down task information from higher cortical ...
  • GC-MS Peak Labeling Under ArMet 
    Jenkins, Helen; Beckmann, Manfred; Draper, John; Hardy, Nigel (Springer, 2007)

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