By Qiang Yang (auth.), Changjie Tang, Charles X. Ling, Xiaofang Zhou, Nick J. Cercone, Xue Li (eds.)
This e-book constitutes the refereed complaints of the 4th overseas convention on complex facts Mining and functions, ADMA 2008, held in Chengdu, China, in October 2008.
The 35 revised complete papers and forty three revised brief papers awarded including the summary of two keynote lectures have been rigorously reviewed and chosen from 304 submissions. The papers concentrate on developments in information mining and peculiarities and demanding situations of actual global purposes utilizing information mining and have unique examine leads to facts mining, spanning functions, algorithms, software program and platforms, and diversified utilized disciplines with strength in info mining.
Read Online or Download Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings PDF
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Additional resources for Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings
5, Naïve Bayes and ID3. Table 1 tabulates the performance of the system. Table 1. 5 65,06 60,47 62,765 ID3 68,71 64,58 66,64 The results can also be tabulated in Figure 2. As it can be seen, the Bayesian networks approach outperforms all the other algorithms by a varying factor of 5% to 11%. This supports our initial claim the Bayesian networks can effective encode the dependency assumptions of the input variables. On the contrast, the naïve Bayesian method is performing worse, due to the unrealistic assumption on the variable independency.
The weak hypotheses should be carefully designed to meet the requirement. In this paper, we make use of the conventional AdaBoost with 30 rounds as the weak hypothesis. The choice is based on several considerations: First, AdaBoost performs well on instance level classiﬁcation problems and the performance is also desirable when evaluated on group level. Second, AdaBoost can be adapted to weighted training set easily. Last, AdaBoost is eﬃcient and easy to implement, which are desirable properties for weak hypothesis.
The choice is based on several considerations: First, AdaBoost performs well on instance level classiﬁcation problems and the performance is also desirable when evaluated on group level. Second, AdaBoost can be adapted to weighted training set easily. Last, AdaBoost is eﬃcient and easy to implement, which are desirable properties for weak hypothesis. Group algorithm to the problem of acronym-expansion extraction. We also experimentally investigated the reason of our approaches outperforming baselines.