By Ghiani G., Laporte G.
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Extra info for A Branch-and-cut Algorithm for the Undirected Rural Postman Problem
The comparison of the other versions of QProber with their DS analogs reveals that QProber generally performs better than DS and that sampling using random queries is inferior to using a focused, carefully chosen set of queries learned from training examples. An interesting conclusion from our experiments is that the new version of DS that retrieves a constant number of documents from each database performs much better than the old version, DS99. The results for DS99 were consistently worse than those for DS because DS99 usually stops before retrieving as many documents as DS, and hence it does not manage to create a good representative profile of the databases.
5 classifiers with FS=off. 5 anyway. We performed the same experiment for the five different versions of DS as well. Since the conclusions from the experiments were similar, in the following we only report the results for the tuning of QProber. For evaluation, we used the F1 -measure for the flat set of categories associated with each classifier and for each of the 100 databases that contained documents in the categories in question. We compared the average performance of the classifiers over the training set.
D) Calculate the similarity of these N documents with the query. The average similarity will be the similarity of the database with category Ci . 2. Rank the categories in order of decreasing similarity with the database. 3. Assign the database to the top-K ranked categories from the hierarchy. To create the concept queries of Step 1, we augmented our hierarchy with an extra level of “titles,” as described in [WMY00]. For Step 1(c) we used the value N = 10, as recommended by the authors. idf weighting [SB88].
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