Nickerson, A., Japkowicz, N. and Milios, E.: 2001, Using unsupervised learning to guide resampling in imbalanced data sets, Proceedings of the Eighth International Workshop on Articial Intelligence and Statistics. Home/Search Document Not in Database Summary Related Articles Check |
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On a Recursive Spectral Algorithm for Clustering from.. - Cheng, Kannan.. (Correct)
....where: P j = j C i j R j = jC i j 6 The F measure of the clustering is de ned as: F (i) jC i j jCj The F measure score is in the range [0; 1] and a higher F measure score implies a better clustering. For a more in depth introduction and justi cation to the F measure, see e.g. [31, 21, 29, 10, 24, 34]. 2. Entropy: For each C j , we de ne the entropy of C j as: E( log The entropy of a cluster is a measure of the disorder within the cluster. As such, a lower entropy score for a clustering implies that the clustering is better. The best possible entropy score is 0, while the worst is ....
....to cluster all 8; 654 news articles into 135 distinct new topics. Each of the news articles was kept fully intact, so the clustering algorithm was just given the raw document term matrix. This same experiment was performed in [10, 21] The second experiment was the same experiment conducted in [24]. Here, the clustering algorithm was only given the 6,575 news articles from 10 of the 135 largest news topics. The results of our algorithm, along with the prior experimental results can be found in Table 1. The measure of performance in the table is the F measure of the clustering. Our algorithm ....
Adam Nickerson, Nathalie Japkowicz, and Evangelos Milios. Using unsupervised learning to guide re-sampling in imbalanced data sets. In Proceedings of the Eighth International Workshop on AI and Statitsics, pages 261-265, 2001.
Asymmetric Missing-Data Problems: Overcoming the Lack of.. - Aleksander Kocz And (2002) (Correct)
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Nickerson, A., Japkowicz, N. and Milios, E.: 2001, Using unsupervised learning to guide resampling in imbalanced data sets, Proceedings of the Eighth International Workshop on Articial Intelligence and Statistics.
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