By Liang Wu, Yuanchun Zhou, Fei Tan, Fenglei Yang (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)
The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed complaints of the seventh overseas convention on complex information Mining and purposes, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers provided including three keynote speeches have been conscientiously reviewed and chosen from 191 submissions. The papers conceal quite a lot of subject matters featuring unique examine findings in information mining, spanning purposes, algorithms, software program and structures, and utilized disciplines.
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Additional resources for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part II
We presume this happen because the error in model prediction that mention earlier have negative impact on this method even if using average value improve some accuracy of it. We found CDE-EM-AVG performance is better than above methods. This means using average value can improve overall accuracy. However, due to burden from the model that built from cost of class distribution very different from test data, the accuracy is not improved much. CDE-EM-EX method performs quite well we presume that using many models can reduce some bias and by remove model that really not suitable for actual data we can improve some accuracy.
Springer, Heidelberg (2008) 3. html 4. : Semi-Supervised Learning. MIT Press, Cambridge (2006) 5. : SMOTE: Synthetic Minority Oversampling Technique. Journal of Artificial Intelligence Research, 16 (2002) 6. : The foundations of cost-sensitive learning. In: The Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 973–978 (2001) 7. : Quantifying counts and costs via classification. Data Mining Knowledge Discovery 17(2) (2008) 8. : Counting Positives Accurately Despite Inaccurate Classification.
1 Tuple-Level and Dimension-Level Uncertainty In fact, there are many existing categories of assumption to model the uncertainty in data stream . In this paper, our assumption is mainly focus on discrete probability density function which has been widely used and easy to apply in practice. For each an uncertain tuple , its | | possible values in d-th dimension can be defined by probability distribution vector as , , , ,…, , , and ∑ p x 1. Evolution-Based Clustering Technique for Heterogeneous Data Streams Dimension-level uncertainty of the j-th dimension of a tuple can be defined as follows: 0, : log 29 denoted by 1 , otherwise ; Let be a tuple, a tuple-level uncertainty of denoted by is an average of its ∑ dimension-level uncertainties defined as ; Therefore, uncertainty of all k-tuples data streams can be calculated as average of their tuple-level uncertainties.