Relational Knowledge Transfer

With relational transfer, it is the relationship among data from a source domain to a target domain that is transferred [1]. In our experiments so far, we are looking at synonym learning (source and target with different features) based in relational transfer.

Experiment

Data 

The source data is a combination of the following OO data: poi-3.0 ant-1.7 camel-1.6 ivy-2.0 jEdit-4.1 and the target data is jm1 (Halstead metrics).

Procedure

• x% of the target is labelled and all others are unlabeled.
• Only 50% of the target data are used as test instances (these are from the unlabeled bunch).
• BORE is applied separately to the labelled x% from the target and the source data.
• Each instance now has a score that is the product of the ranks from the power ranges (the scores are normalized).
• Each target instance gets a BORE score by using the ranks from the x%.
• These are then matched to their nearest [instances scores] from the source data and the majority defect label is assigned to the target instance.
• For the within experiment, the x% of labelled target data is used as the train set and the 50% test instances are the test.
• The above is also benchmarked with a 10 x 10 cross-validation experiment on jm1 with Naive Bayes.

Initial Results

Click here

or syns.pdf

So far there are four things offered

1. Synonyms - (if technology, or data collect methods, or metrics change, can we still use previous projects).
2. Cross Prediction method for synonyms based on relational transfer of different data-sets.
3. The percentage of labelled data used - second opinion paper is at 6% for the lowest and mixed paper experiments with 10%
4. Methods closely resembles the second opinion paper, BORE is linear. 

Reference

[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." Knowledge and Data Engineering, IEEE Transactions on 22.10 (2010): 1345-1359.