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IR 2009/2010 Weka Experiments Niek de Moel

Aproach 1/2 Create basics Amount of documents for testing (500/1000/2000/4000/8000/1600/full) Define min & max document frequentie “How to select key attributes/features? Best predictor?”

Aproach 2/2 Testing Using cross validation. Data: F-Measure (precission / recall) ROC (Receiver operating characteristic) TP vs FP Classifier errors Correctly VS Incorrectly Classified Instances

Results so far Test corpus size does matter in speed, not in results. Min document freq 1-10: No pattern.. Around 35 for corpus 500. Still working on this. Classifying nova- 2 vandaag – netwerk is difficult (lot’s of simularities). Proceeding: ,[object Object]

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Ir Presentation

1. IR 2009/2010 Weka Experiments Niek de Moel

2. Aproach 1/2 Create basics Amount of documents for testing (500/1000/2000/4000/8000/1600/full) Define min & max document frequentie “How to select key attributes/features? Best predictor?”

3. Aproach 2/2 Testing Using cross validation. Data: F-Measure (precission / recall) ROC (Receiver operating characteristic) TP vs FP Classifier errors Correctly VS Incorrectly Classified Instances

5. Does Experiment Environment work?

6. Class attribute was not numeric...

7. Are iteration needed?

8. Compare algorithms.

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