Difference between revisions of "Deliverable 5.2"
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| + | Evaluation has been done currently based on the [http://www.cs.bris.ac.uk/Research/Micro/beebs.jsp BEEBS] framework available for sample evaluation which will be extended for the training set later on. | ||
In terms of validation methods, the following are good candidates: | In terms of validation methods, the following are good candidates: | ||
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| − | This leaves a choice between J48, KNN & SVM to be made by the end of September. We will do this taking into account what our training data will be, how long these algorithms will take to train as well as practicality, performance and reliability. Currently research is tending towards J48 due to its simplicity, ease to translate to C code and very fast creation and evaluation. | + | This leaves a choice between J48, KNN & SVM to be made by the end of September. We will do this taking into account what our training data will be, how long these algorithms will take to train, as well as practicality, performance and reliability. Currently research is tending towards J48 due to its simplicity, ease to translate to C code and very fast creation and evaluation of test data. |
Revision as of 07:22, 1 September 2013
Deliverable 5.2: Selection of Core Machine Learning Algorithms
Status: Ongoing, options identified
Via experimentation, primarily using the WEKA framework, along with discussions from the MILEPOST team, we have already identified the following as possible core machine learning algorithms:
- Decision Tree J48
- KNN
- SVM
Evaluation has been done currently based on the BEEBS framework available for sample evaluation which will be extended for the training set later on.
In terms of validation methods, the following are good candidates:
- 10 Cross Fold Validation
- Leave One Out Validation
Of these, the following have been discarded as unsuitable:
- SVM - Will take too long to train on the data
- Leave One Out Validation
It has been researched that LOOV is not quite as optimal as 10 Cross Fold, and there will be added benefits of 10 Cross Fold being faster in this case. Both models are approximately unbiased, with 10 fold having slightly less variance which is preferred. (Efron, 1983)
This leaves a choice between J48, KNN & SVM to be made by the end of September. We will do this taking into account what our training data will be, how long these algorithms will take to train, as well as practicality, performance and reliability. Currently research is tending towards J48 due to its simplicity, ease to translate to C code and very fast creation and evaluation of test data.
