ARTIFICIAL DATA GENERATION FOR ONE-CLASS CLASSIFICATION - A Case Study of Dimensionality Reduction for Text and Biological Data

Santiago D. Villalba, Pádraig Cunningham

2009

Abstract

Artificial negatives have been employed in a variety of contexts in machine learning to overcome data availability problems. In this paper we explore the use of artificial negatives for dimension reduction in one-class classification, that is classification problems where only positive examples are available for training. We present four different strategies for generating artificial negatives and show that two of these strategies are very effective for discovering discriminating projections on the data, i.e., low dimension projections for discriminating between positive and real negative examples. The paper concludes with an assessment of the selection bias of this approach to dimension reduction for one-class classification.

Paper Citation

in Harvard Style

D. Villalba S. and Cunningham P. (2009). ARTIFICIAL DATA GENERATION FOR ONE-CLASS CLASSIFICATION - A Case Study of Dimensionality Reduction for Text and Biological Data . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 202-210. DOI: 10.5220/0002310202020210

in Bibtex Style

@conference{kdir09,
author={Santiago D. Villalba and Pádraig Cunningham},
title={ARTIFICIAL DATA GENERATION FOR ONE-CLASS CLASSIFICATION - A Case Study of Dimensionality Reduction for Text and Biological Data},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},
year={2009},
pages={202-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002310202020210},
isbn={978-989-674-011-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
TI - ARTIFICIAL DATA GENERATION FOR ONE-CLASS CLASSIFICATION - A Case Study of Dimensionality Reduction for Text and Biological Data
SN - 978-989-674-011-5
AU - D. Villalba S.
AU - Cunningham P.
PY - 2009
SP - 202
EP - 210
DO - 10.5220/0002310202020210