Dimensionality Reduction through Sub-Space Mapping for Nearest Neighbour Algorithms
Payne, Terry R. and Edwards, Peter (2000) Dimensionality Reduction through Sub-Space Mapping for Nearest Neighbour Algorithms. In, The 10th European Conference on Machine Learning , 331-343.
Many learning algorithms make an implicit assumption that all the attributes present in the data are relevant to a learning task. However, several studies have demonstrated that this assumption rarely holds; for many supervised learning algorithms, the inclusion of irrelevant or redundant attributes can result in a degradation in classification accuracy. While a variety of different methods for dimensionality reduction exist, many of these are only appropriate for datasets which contain a small number of attributes (e.g. < 20). This paper presents an alternative approach to dimensionality reduction, and demonstrates how it can be combined with a Nearest Neighbour learning algorithm. We present an empirical evaluation of this approach, and contrast its performance with two related techniques; a Monte-Carlo wrapper and an Information Gain-based filter approach.
|Item Type:||Conference or Workshop Item (Paper)|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science
|Date Deposited:||07 Nov 2007|
|Last Modified:||14 Aug 2012 01:49|
|Contributors:||Payne, Terry R. (Author)
Edwards, Peter (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||0|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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