By Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)
This booklet constitutes the refereed complaints of the eleventh Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2007, held in Nanjing, China in may possibly 2007.
The 34 revised complete papers and ninety two revised brief papers offered including 4 keynote talks or prolonged abstracts thereof have been rigorously reviewed and chosen from 730 submissions. The papers are dedicated to new rules, unique study effects and sensible improvement stories from all KDD-related parts together with information mining, computing device studying, databases, data, information warehousing, information visualization, computerized medical discovery, wisdom acquisition and knowledge-based systems.
Read or Download Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings PDF
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Additional resources for Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings
For SVMs the method to extract a confidence range is straightforward. For the two-class case, we can use the output of the SVM as distance to the separating hyperplane. In the case of multi-class SVMs, the minimum distance to all of the used hyperplanes is considered. For other classification paradigms, the calculation is less straightforward. In general, the confidence range of an object o can be determined by Multi-represented Classification Based on Confidence Estimation 27 taking the minimum distance for which the class prediction changes from class cpred to some other class cother .
20 B. Andreopoulos, A. An, and X. Wang Table 1. ) HAI. 5% 72% zoo (7 Entr. 01 0 soybean-data (19 HAI. Entr. 1 HIERDENC Results. Table 1 shows the HIERDENC results for these datasets before and after cutting the tree. After cutting the HIERDENC tree for zoo, its HA Indexes, Entropy, and AIC are slightly better than CLICKS. The HIERDENC results for soybean-data are signiﬁcantly better than CLICKS. The Entropy is naturally lower (better) in results with many clusters; by comparing results of algorithms with similar numbers of clusters, the HIERDENC Entropy is often lower.
Our experimental evaluation illustrate the capability of our new approach to improve the classification accuracy compared to combined classifiers that employ distribution vectors. The rest of the paper is organized as follows. Section 2 surveys related work. In section 3, we introduce the general idea for our method of classifier combination. Afterwards, section 4 describes methods to derive confidence ranges for various classifiers and explains their use for deriving confidence estimates. The results of our experimental evaluation are shown in section 5.
Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings by Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)