Browsing by Author "Mac Parthaláin, Neil"

H...............H

Browsing by Author "Mac Parthaláin, Neil"

Sort by: Order: Results:

  • Mac Parthaláin, Neil; Shen, Qiang; Jensen, Richard (2008-01-22)
    Feature Selection (FS) methods based on fuzzy-rough set theory (FRFS) have employed the dependency function to guide the FS process with much success. More recently a method has been developed which uses fuzzy-entropy ...
  • Jensen, Richard; Shen, Qiang; Mac Parthaláin, Neil (2010-01-18)
    Feature Selection (FS) or Attribute Reduction techniques are employed for dimensionality reduction and aim to select a subset of the original features of a dataset which are rich in the most useful information. The benefits ...
  • Shen, Qiang; Mac Parthaláin, Neil; Jensen, Richard (2007-12-05)
    Feature Selection (FS) is a technique for dimensionality reduction. Its aims are to select a subset of the original features of a dataset which are rich in the most useful information. The benefits include improved data ...
  • Mac Parthaláin, Neil; Shen, Qiang (2009-01-21)
    Of all of the challenges which face the effective application of computational intelligence technologies for pattern recognition, dataset dimensionality is undoubtedly one of the primary impediments. In order for pattern ...
  • Wu, Wei; Shen, Qiang; Qu, Yanpeng; Mac Parthaláin, Neil (2010-09-23)
    The assessment of mammographic risk analysis is an important issue in the medical field. Various approaches have been applied in order to achieve a higher accuracy in such analysis. In this paper, an approach known as ...
  • 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 ...
  • 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 ...
  • Shen, Qiang; Mac Parthaláin, Neil; Jensen, Richard (2008-09-29)
    The accuracy of methods for the detection of mammographic abnormaility is heavily related to breast tissue characteristics. A breast with high tissue density will have reduced sensitivity in terms of detection. Also, breast ...
  • Qu, Yanpeng; Shang, Changjing; Mac Parthaláin, Neil; Wu, Wei; Shen, Qiang (2011-09-06)
    Fuzzy-rough sets play an important role in dealing with imprecision and uncertainty for discrete and real-valued or noisy data. However, there are some problems associated with the approach from both theoretical and practical ...
  • Jensen, Richard; Mac Parthaláin, Neil (2010-01-11)
    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 ...
  • Mac Parthaláin, Neil; Jensen, Richard (2011-04-06)
    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 ...
  • Mac Parthaláin, Neil; Shen, Qiang (2010-11-29)
    Rough set theory (RST) has enjoyed an enormous amount of attention in recent years and has been applied to many real-world problems including data mining, pattern recognition, and intelligent control. Much research has ...
  • Mac Parthaláin, Neil (Aberystwyth University, 2009-09-23)
    Rough set theory (RST) was proposed as a mathematical tool to deal with the analysis of imprecise, uncertain or incomplete information or knowledge. It is of fundamental importance to artificial intelligence particularly ...

Search Cadair

Browse

My Account