Now showing 1 - 3 of 3
  • Publication
    k-Nearest Neighbour Classifiers
    (University College Dublin. School of Computer Science and Informatics, 2007-03-27) ;
    Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data.
      53
  • Publication
    Featureless Similarity
    (University College Dublin. School of Computer Science and Informatics, 2007-02-23) ;
    Assessing the similarity between cases is a key aspect of the retrieval phase in Case-Based Reasoning (CBR). In most CBR work, similarity is assessed based on feature-value descriptions of cases using similarity metrics which use these feature values. In fact it might be said that this notion of a feature-value representation is a defining part of the CBR world-view – it underpins the idea of a problem space with cases located relative to each other in this space. Recently a variety of similarity mechanisms have emerged that are not feature-based. Some of these ideas have emerged in CBR research but many of them have arisen in other areas of data analysis. In fact research on Support Vector Machines(SVM) is a rich source of novel similarity representations because of the emphasis on encoding domain knowledge in the kernel function of the SVM. In this paper we review these novel featureless similarity measures and assess the implications these measures have for CBR research.
      25
  • Publication
    Active learning for text classification with reusability
    Where active learning with uncertainty sampling is used to generate training sets for classification applications, it is sensible to use the same type of classifier to select the most informative training examples as the type of classifier that will be used in the final classification application. There are scenarios, however, where this might not be possible, for example due to computational complexity. Such scenarios give rise to the reusability problem—are the training examples deemed most informative by one classifier type necessarily as informative for a different classifier types? This paper describes a novel exploration of the reusability problem in text classification scenarios. We measure the impact of using different classifier types in the active learning process and in the classification applications that use the results of active learning. We perform experiments on four different text classification problems, using the three classifier types most commonly used for text classification. We find that the reusability problem is a significant issue in text classification; that, if possible, the same classifier type should be used both in the application and during the active learning process; and that, if the ultimate classifier type is unknown, support vector machines should be used in active learning to maximise reusability.
      470Scopus© Citations 35