Browsing Cyfrifiadureg / Computer Science by Author "Jensen, Richard"

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Browsing Cyfrifiadureg / Computer Science by Author "Jensen, Richard"

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  • 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 ...
  • Jensen, Richard (2011-12-31)
  • Jensen, Richard; Shen, Qiang (2005-01-01)
    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. ...
  • 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 ...
  • 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 ...
  • Jensen, Richard; Cornelis, Chris (2011-12-31)
  • 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 ...
  • 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, ...
  • 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 ...
  • 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 ...
  • Cornelis, Chris; Jensen, Richard; Shen, Qiang (2009)
    The automated generation of feature pattern-based if-then rules is essential to the success of many intelligent pattern classifiers, especially when their inference results are expected to be directly human-comprehensible. ...
  • Shen, Qiang; Jensen, Richard (2008)
    One of the many successful applications of rough set theory has been to the area of feature selection. The rough set ideology of using only the supplied data and no other information has many benefits, where most other ...
  • Jensen, Richard; Shen, Qiang (2009)
    One of the many successful applications of rough set theory has been to the area of feature selection. The rough set principle of using only the supplied data and no other information has many benefits, where most other ...
  • MacParthaláin, Neil Seosamh; Jensen, Richard (IEEE, 2009-11-30)
    For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors to reflect decision class labels. It is ...
  • 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 ...
  • 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 ...
  • 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 ...

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