Browsing by Author "Jensen, Richard"

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  • Tuson, Andrew; Shen, Qiang; Jensen, Richard (2010)
    This paper describes a novel, principled approach to real-valued dataset reduction based on fuzzy and rough set theory. The approach is based on the formulation of fuzzy-rough discernibility matrices, that can be transformed ...
  • Jensen, Richard; MacParthalain, Neil; Cornelis, Chris (IEEE Press, 2014)
    Data dimensionality has become a pervasive problem in many areas that require the learning of interpretable models. This has become particularly pronounced in recent years with the seemingly relentless growth in the size ...
  • Goodarzi, Mohammad; Freitas, Matheus P.; Jensen, Richard (2009)
    Few variables were selected from a pool of calculated Dragon descriptors through three different feature selection methods, namely genetic algorithm (GA), successive projections algorithm (SPA), and fuzzy rough set ant ...
  • Goodarzi, Mohammad; Freitas, Matheus P.; Jensen, Richard (2009-04-01)
    Few variables were selected from a pool of calculated Dragon descriptors through three different feature selection methods, namely genetic algorithm (GA), successive projections algorithm (SPA), and fuzzy rough set ant ...
  • Yang, Jie; Xia, W.; Wang, Xiangyang; Jensen, Richard; Teng, X. (2007)
    We propose a new feature selection strategy based on rough sets and Particle Swarm Optimization (PSO). Rough sets has been used as a feature selection method with much success, but current hill-climbing rough set approaches ...
  • Shen, Qiang; Jensen, Richard (2009)
    The evaluation of glass evidence in forensic science is an important issue. Traditionally, this has depended on the comparison of the physical and chemical attributes of an unknown fragment with a control fragment. A high ...
  • Moayedikia, Alireza; Ong, Kok-Leong; Boo, Yee Ling; Yeoh, William G. S.; Jensen, Richard (2017-01-01)
    Misclassification costs of minority class data in real-world applications can be very high. This is a challenging problem especially when the data is also high in dimensionality because of the increase in overfitting and ...
  • Slezak, Dominik; Cornelis, Chris; Hurtado Martín, German; Jensen, Richard (2008-06-13)
    In this paper, within the context of fuzzy rough set theory, we generalize the classical rough set framework for data-based attribute selection and reduction, based on the notion of fuzzy decision reducts. Experimental ...
  • Cornelis, Chris; Hurtado Martín, Germán; Jensen, Richard; Ślȩzak, Dominik (Springer Nature, 2008)
    In this paper, within the context of fuzzy rough set theory, we generalize the classical rough set framework for data-based attribute selection and reduction, based on the notion of fuzzy decision reducts. Experimental ...
  • Jensen, Richard; Mac Parthaláin, Neil; Shen, Qiang (2008-07-03)
    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 ...
  • 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 ...
  • Jensen, Richard; Tuson, Andrew; Shen, Qiang (2014-01-10)
    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; 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. ...
  • Mac Parthaláin, Neil; Jensen, Richard; Shen, Qiang (2007-12-05)
    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 ...
  • 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 ...
  • Verbiest, Nele; Cornelis, Chris; Jensen, Richard (IEEE Press, 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, ...
  • 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 ...
  • Jensen, Richard (2011-12-31)