On boosting and noisy labels

Boosting is a machine learning technique widely used across many disciplines. Boosting enables one to learn from labeled data in order to predict the labels of unlabeled data. A central property of boosting instrumental to its popularity is its resistance to overfitting. Previous experiments provide a margin-based explanation for this resistance to overfitting. In this thesis, the main finding is that boosting's resistance to overfitting can be understood in terms of how it handles noisy (mislabeled) points. Confirming experimental evidence emerged from experiments using the Wisconsin Diagnostic Breast Cancer(WDBC) dataset commonly used in machine learning experiments. A majority vote ensemble filter identified on average that 2.5% of the points in the dataset as noisy. The experiments chiefly investigated boosting's treatment of noisy points from a volume-based perspective. While the cell volume surrounding noisy points did not show a significant difference from other points, the decision volume surrounding noisy points was two to three times less than that of non-noisy points. Additional findings showed that decision volume not only provides insight into boosting's resistance to overfitting in the context of noisy points, but also serves as a suitable metric for identifying which points in a dataset are likely to be mislabeled.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Chan, Jeffrey (Jeffrey D.)

Advisor dc:contributor.advisor

• Patrick H. Winston.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/100297

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/100297