Date of Award

2013

Publisher

North Dakota State University

Document Type

Thesis

Degree Awarded

Master of Science (MS)

Program

Computer Science

Department

Computer Science

College

Engineering

Faculty Advisor

Denton, Anne M.

Description

In this work, we present a novel algorithm that considers attributes from different experimental sources as separate groups for the purpose of classification. We remove noise from each of these groups, combine them, and then run the classifier on the grouped data. Examples are considered to be noise if they do not contribute to the prediction but, rather, degrade the quality of the classification result. As part of this work, we identify a measure that appropriately labels noise without knowledge of the class labels. Our method shows that the classification result is better when run on such filtered, grouped data than when run on the entire grouped data. In this work, we have considered time-series data because of their noisy nature. Our approach can be viewed as unsupervised feature-subset selection in grouped attributes and at the level of each instance individually.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/26863

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