loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Santiago D. Villalba and Pádraig Cunningham

Affiliation: University College Dublin, Ireland

ISBN: 978-989-674-011-5

Keyword(s): Dimensionality reduction, One-class classification, Novelty detection, Locality preserving projections, Text classification, Functional genomics.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining High-Dimensional Data ; Mining Text and Semi-Structured Data ; Pre-Processing and Post-Processing for Data Mining ; Soft Computing ; Symbolic Systems

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.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.206.48.142

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
D. Villalba S.; Cunningham P. and (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

@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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.