Browsing by Title

Sort by: Order: Results:

  • 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 (2008-07-03)
    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; 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 (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 ...
  • 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 ...
  • Shen, Qiang; Boongoen, Tossapon (2012-04)
    Alias detection has been the significant subject being extensively studied for several domain applications, especially intelligence data analysis. Many preliminary methods rely on text-based measures, which are ineffective ...
  • 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 ...
  • Liu, Honghai; Coghill, George; Barnes, Dave (2009-12)
    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 ...
  • 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 (2009-10-22)
    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 ...
  • 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 ...
  • Qu, Yanpeng; Shen, Qiang; MacParthaláin, Neil Seosamh; Shang, Changjing; Wu, Wei (2013-01)
    Fuzzy-rough sets have enjoyed much attention in recent years as an effective way in which to extend rough set theory such that it can deal with real-valued data. More recently, fuzzy-rough sets have been employed for the ...
  • MacParthaláin, Neil Seosamh; Jensen, Richard; Shen, Qiang; Zwiggelaar, Reyer (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 ...
  • 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 ...
  • Diao, Ren; Shen, Qiang (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 ...
  • Diao, Ren; Shen, Qiang (IEEE, 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 ...