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Choosing feature sets for training and testing self-organising maps: A Case Study (2001)
| Venue: | Neural Computing and Applications |
| Citations: | 9 - 4 self |
BibTeX
@ARTICLE{Ahmad01choosingfeature,
author = {Khurshid Ahmad and Bogdan L. Vrusias and Anthony Ledford},
title = {Choosing feature sets for training and testing self-organising maps: A Case Study},
journal = {Neural Computing and Applications},
year = {2001},
volume = {10},
pages = {56--66}
}
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Abstract
Statistical pattern recognition techniques, supervised and unsupervised classification techniques being two good examples here, rely on the computations of similarity and distance metrics. The distances are computed in a multi-dimensional space. The axes of this space in principle relate to the features inherent in the input data. Usually such features are chosen by neural network developers, thereby introducing a possible bias. A method of automatically generating feature sets is discussed, with specific reference to the categorisation of streams of free-text news items. The feature sets were generated by a procedure that automatically selects a group of keywords based on a lexico-semantic analysis. Three different types of text streams -- headlines only, news summaries and full news items including the body of the text -- have been categorised using self-organising feature maps (SOFM) [2]. A method for assessing the discrimination ability of a SOFM, based on Fisher's Linear Discriminant Rule suggests that an SOFM trained on vectors related to summaries only provides a fairly accurate cluster when compared with vectors related to full text. The use of summaries as document surrogates for document categorisation is suggested.
Keyphrases
feature set case study self-organising map principle relate input data full text text stream lexico-semantic analysis statistical pattern recognition technique different type possible bias good example specific reference self-organising feature map document categorisation news summary unsupervised classification technique distance metric neural network developer linear discriminant rule document surrogate multi-dimensional space discrimination ability accurate cluster free-text news item full news item
