Decomposition of high dimensional pattern spaces for hierarchical classification
Kybernetika (1998)
- Volume: 34, Issue: 4, page [435]-442
- ISSN: 0023-5954
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topKumar, Rajeev, and Rockett, Peter I. "Decomposition of high dimensional pattern spaces for hierarchical classification." Kybernetika 34.4 (1998): [435]-442. <http://eudml.org/doc/33374>.
@article{Kumar1998,
abstract = {In this paper we present a novel approach to decomposing high dimensional spaces using a multiobjective genetic algorithm for identifying (near-)optimal subspaces for hierarchical classification. This strategy of pre-processing the data and explicitly optimising the partitions for subsequent mapping onto a hierarchical classifier is found to both reduce the learning complexity and the classification time with no degradation in overall classification error rate. Results of partitioning pattern spaces are presented and compared with various algorithms.},
author = {Kumar, Rajeev, Rockett, Peter I},
journal = {Kybernetika},
keywords = {pre-processing; decomposition; pattern classifiers; pre-processing; decomposition; pattern classifiers},
language = {eng},
number = {4},
pages = {[435]-442},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Decomposition of high dimensional pattern spaces for hierarchical classification},
url = {http://eudml.org/doc/33374},
volume = {34},
year = {1998},
}
TY - JOUR
AU - Kumar, Rajeev
AU - Rockett, Peter I
TI - Decomposition of high dimensional pattern spaces for hierarchical classification
JO - Kybernetika
PY - 1998
PB - Institute of Information Theory and Automation AS CR
VL - 34
IS - 4
SP - [435]
EP - 442
AB - In this paper we present a novel approach to decomposing high dimensional spaces using a multiobjective genetic algorithm for identifying (near-)optimal subspaces for hierarchical classification. This strategy of pre-processing the data and explicitly optimising the partitions for subsequent mapping onto a hierarchical classifier is found to both reduce the learning complexity and the classification time with no degradation in overall classification error rate. Results of partitioning pattern spaces are presented and compared with various algorithms.
LA - eng
KW - pre-processing; decomposition; pattern classifiers; pre-processing; decomposition; pattern classifiers
UR - http://eudml.org/doc/33374
ER -
References
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- Kumar R., Feature Selection, Representation and Classification in Vision, Ph.D. Thesis, Dept. Electronic and Electrical Engineering, University of Sheffield, 1997
- al C. C. Taylor et, Dataset descriptions and results, In: Machine Learning, Neural and Statistical Classification (D. Michie, D. J. Spiegelhalter and C. C. Taylor, eds.), Ellis Horwood, London 1994, pp. 131–174 (1994)
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