Classification in E-Procurement - University of Reading

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Why SVMs do worse Consider key differences PO has 2,179,122 documents, Reuters has 9,495 PO has 909 classes, Reuters has 66 PO ~ 8.04 features per doc, Reuters ~62.78 Each feature in the PO data appears in an average of 325.59 documents: in Reuters the figure is 19.38 PO data contains appreciable label noise (where classes are misclassified), the Reuters data does not. To evaluate significances of these Project PO data into Reuters, so share characteristics. Classification in E-Procurement- 12 Presented at CIS 2012 © Dr Richard Mitchell 2012
Projecting PO Data into Reuters 1.0 PO Projection 1.0 PO Projection 0.8 0.8 Macro F 0.6 0.4 0.2 Micro F 0.6 0.4 0.2 0.0 0.0 NH 2 NH 1 SVM Roc NB k-NN C4.5 NH 2 NH 1 SVM Roc NB k-NN C4.5 Classifier Classifier Suggest SVM good as retained performance from basic Reuters data C4.5, KNN, NB retained performance from PO data Classification in E-Procurement- 13 Presented at CIS 2012 © Dr Richard Mitchell 2012
Page 1 and 2: Classification in E-Procurement P.J
Page 3 and 4: The three KTPs Three linked project
Page 5 and 6: Companies Products Matching Product
Page 7 and 8: Extension for Carbon Footprint Spen
Page 9 and 10: Methods Tried K-nearest Neighbour (
Page 11: Micro-Average F Measure 1.0 PO Data

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