By Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng
This e-book constitutes the lawsuits of the twelfth foreign convention on complex info Mining and functions, ADMA 2016, held in Gold Coast, Australia, in December 2016.
The 70 papers provided during this quantity have been rigorously reviewed and chosen from a hundred and five submissions. the chosen papers coated a wide selection of significant themes within the region of knowledge mining, together with parallel and dispensed information mining algorithms, mining on information streams, graph mining, spatial information mining, multimedia info mining, internet mining, the web of items, healthiness informatics, and biomedical facts mining.
Read Online or Download Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings PDF
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Extra resources for Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings
Neurocomputing 71(7), 1578–1594 (2008) 18 C. Bartley et al. 13. : A regularized monotonic fuzzy support vector machine for data mining with prior knowledge. IEEE Trans. Fuzzy Syst. PP(99) (2014) 14. : UCI machine learning repository (2013). edu/ ml 15. : Nonlinear knowledge-based classiﬁcation. IEEE Trans. Neural Netw. 19(10), 1826–1832 (2008) 16. : Rank discrimination measures for enforcing monotonicity in decision tree induction. Inf. Sci. 291, 143–171 (2015) 17. : Generating noisy monotone ordinal datasets.
Bartley et al. Table 2. Accuracy summary for N = 50. 05. respectively. 8 % (AD). Sample size eﬀect can be seen in Fig. 3. Broadly the datasets can be grouped into three types. Car and WBCDiag show little improvement as discussed. Cleveland, SA Heart and German datasets show diminishing returns as the sample size increases, which is typically expected for incorporating domain knowledge. Interestingly, for the remaining datasets (Ljubjlana, Autompg, Haberman, Pima) the beneﬁt from monotonicity is maintained with increasing sample size, even when the maximum training data is used, suggesting perhaps that noise is obscuring the monotone relationships rather than sample size.
5 Results and Discussion Classifier Partial Monotonicity. Figure 2 shows the impact of the constraint technique and sample size on classiﬁer monotonicity (M CCf eatavg ). Firstly we note that the unconstrained car and WBCdiag models were almost perfectly monotone with no constraints! For the other datasets, it is clear that the two proposed approaches are more eﬀective at inducing a monotone classiﬁer than the existing CJ1 approach for all datasets. In fact CJ1 does not noticeably increase monotonicity except for SA Heart, Ljubjlana, Autompg and Haberman, where the increases were less than half those of the proposed techniques.