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The performances of the method are demonstrated with a variety of artificial datasets.
Experiments are undertaken on seven real-world and a set of artificial datasets with various settings of costs.
Finally, the feasibility and effectiveness of L1-LSTBSVM are validated by extensive experimental results on both UCI datasets and artificial datasets.
Experimental evaluations on real datasets as well as artificial datasets show the effectiveness of the proposed incremental updating method compared with the non-incremental method.
Experimental results on large scale artificial datasets show that it compares favorably with a fully centralized implementation, in terms of speed, efficiency and generalization accuracy.
To verify quality of the proposed approaches, several heavy computations have been conducted on artificial datasets and also real gene expression datasets.
We perform several experiments with five artificial datasets.
Generate (e.g. 2,500) artificial datasets of scores, distributed according to the fitted (M_{i}).
These follow-up analyses use artificial datasets recreated from the datasets used in the main study.
T10I4D100K (artificial datasets generated by IBM's data generator) [37] have (10^5) transactions with 870 items in it.
In Table 7, we give the details of the artificial datasets by varying the number of sellers, items and generations.
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