Chapter 14
Building Smarter Transfer Learners
Abstract
In this part of the book Data Science for Software Engineering: Sharing Data and Models, we show that sharing all data is less useful that sharing just the relevant data. There are several useful methods for finding those relevant data regions including simple nearest neighbor, or kNN, algorithms; clustering (to optimize subsequent kNN); and pruning away “bad” regions. Also, we show that with clustering, it is possible to repair missing data in project records.
| Name: | TEAK |
| Also known as: | TEAK could be categoried as a transfer learner or a relevancy filter. It could also be categorized as an instance-based (or case-based) reasoner. |
| Intent: | Generating software effort estimates, when there is ... |
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