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Authors: Thanh-Nghi Do and François Poulet

Affiliation: ESIEA Recherche, France

Keyword(s): Mining very large datasets, Support vector machines, Active learning, Interval data analysis, Visual data mining, Information visualization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: We present a new support vector machine (SVM) algorithm and graphical methods for mining very large datasets. We develop the active selection of training data points that can significantly reduce the training set in the SVM classification. We summarize the massive datasets into interval data. We adapt the RBF kernel used by the SVM algorithm to deal with this interval data. We only keep the data points corresponding to support vectors and the representative data points of non support vectors. Thus the SVM algorithm uses this subset to construct the non-linear model. We also use interactive graphical methods for trying to explain the SVM results. The graphical representation of IF-THEN rules extracted from the SVM models can be easily interpreted by humans. The user deeply understands the SVM models’ behaviour towards data. The numerical test results are obtained on real and artificial datasets.

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Paper citation in several formats:
Do, T. and Poulet, F. (2005). MINING VERY LARGE DATASETS WITH SVM AND VISUALIZATION. In Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 972-8865-19-8; ISSN 2184-4992, SciTePress, pages 127-134. DOI: 10.5220/0002548601270134

@conference{iceis05,
author={Thanh{-}Nghi Do. and Fran\c{C}ois Poulet.},
title={MINING VERY LARGE DATASETS WITH SVM AND VISUALIZATION},
booktitle={Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2005},
pages={127-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002548601270134},
isbn={972-8865-19-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - MINING VERY LARGE DATASETS WITH SVM AND VISUALIZATION
SN - 972-8865-19-8
IS - 2184-4992
AU - Do, T.
AU - Poulet, F.
PY - 2005
SP - 127
EP - 134
DO - 10.5220/0002548601270134
PB - SciTePress

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