Ensemble techniques have recently been used to enhance the performance of machine learning methods. However, current ensemble techniques for classification require both positive and negative data to produce a result that is both meaningful and useful. Negative data is, however, sometimes difficult, expensive or impossible to access. In this thesis a learning framework is described that has a very close relationship to boosting. Within this framework a method is described which bears remarkable similarities to boosting stumps and that does not rely on negative examples. This is surprising since learning from positive-only data has traditionally been difficult. An empirical methodology is described and deployed for testing positive-only learning systems using commonly available multiclass datasets to compare these learning systems with each other and with multiclass learning systems. Empirical results show that our positive-only boosting-like method learns, using stumps as a base learner and from positive data only, successfully, and in the process does not pay too heavy a price in accuracy compared to learners that have access to both positive and negative data. We also describe methods of using positive-only learners on multiclass learning tasks and vice versa and empirically demonstrate the superiority of our method of learning in a boosting-like fashion from positive-only data over a traditional multiclass learner converted to learn from positive-only data. Finally we examine some alternative frameworks, such as when additional unlabelled training examples are given. Some theoretical justifications of the results and methods are also provided.
| Identifer | oai:union.ndltd.org:ADTP/187950 |
| Date | January 2004 |
| Creators | Mitchell, Andrew, Computer Science & Engineering, Faculty of Engineering, UNSW |
| Publisher | Awarded by:University of New South Wales. Computer Science and Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | Copyright Andrew Mitchell, http://unsworks.unsw.edu.au/copyright |
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