Identifying Rare Classes with Sparse Training Data

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Author

Christopher Clifton

Tech report number

CERIAS TR 2007-97

Entry type

conference

Abstract

Building models and learning patterns from a collection of data are essential tasks for decision making and dissemination of knowledge. One of the common tools to extract knowledge is to build a classifier. However, when the training dataset is sparse, it is difficult to build an accurate classifier. This is especially true in biological science, as biological data are hard to produce and error-prone. Through empirical results, this paper shows challenges in building an accurate classifier with a sparse biological training dataset. Our findings indicate the inadequacies in well known classification techniques. Although certain clustering techniques, such as seeded k-Means, show some promise, there are still spaces for further improvement. In addition, we propose a novel idea that could be used to produce more balanced classifier when training data samples are very limited.

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Date

2007 – 09

URL

http://www.springerlink.com/content/5mm3m8gv26v33v2m/

Booktitle

Database and Expert Systems Applications

Key alpha

Clifton

Pages

251-260

Publisher

Springer Berlin / Heidelberg

Volume

4653

Publication Date

2007-09-01

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@Conference{ Clifton,
	title = "Identifying Rare Classes with Sparse Training Data",
	author = "Christopher Clifton",
	year = "2007",
	month = "09",
	booktitle = "Database and Expert Systems Applications",
	pages = "251-260",
	publisher = "Springer Berlin / Heidelberg",
	volume = "4653",
	abstract = "Building models and learning patterns from a collection of data are essential tasks for decision making and dissemination of knowledge. One of the common tools to extract knowledge is to build a classifier. However, when the training dataset is sparse, it is difficult to build an accurate classifier. This is especially true in biological science, as biological data are hard to produce and error-prone. Through empirical results, this paper shows challenges in building an accurate classifier with a sparse biological training dataset. Our findings indicate the inadequacies in well known classification techniques. Although certain clustering techniques, such as seeded k-Means, show some promise, there are still spaces for further improvement. In addition, we propose a novel idea that could be used to produce more balanced classifier when training data samples are very limited.",
	url = "http://www.springerlink.com/content/5mm3m8gv26v33v2m/",
}