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Taking Advantage of Unlabeled Data with the Ordered Classification Algorithm
| Content Provider | Semantic Scholar |
|---|---|
| Author | Solorio, Thamar Fuentes, Olac |
| Abstract | We introduce a new method for improving poor performance of classi£ers due to a small training set. The Ordered Classi£cation algorithm presented here incrementally increases the training set by adding unlabeled examples. These unlabeled examples are selected by the algorithm accordingly to the con£dence level of the predictions made by an ensemble of classi£ers. The use of this con£dence level measurement, which was inspired by the Query By Committee approach within the Active Learning setting, ensures that the algorithm incorporates the examples which are more likely to have the right classi£cation label assigned by the ensemble. Experimental results show that this algorithm effectively takes advantage of the unlabeled data yielding an error reduction of up to 78%. Giving that a very common scenario in classi£cation problems is the lack of a large enough training set, this algorithm provides a practical solution. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cs.utep.edu/ofuentes/TS_OF_IASTED02.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
