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Test results based on two real-world datasets validate the effectiveness of the proposed method.
We empirically demonstrate the usability of the implemented algorithms in different domain scenarios, based on two real-world datasets of configurations.
Based on two real-world crowdfunding datasets, our experimental results reveal that the proposed framework outperforms a classical LDA-based method in predicting fund raising success by an average of 11% in terms of F1 score.
Finally, based on four real-world data sets (i.e., CDBLP, Facebook, Weibo, and P2P), we verify our theoretical findings.
The experimental results, based on twenty real-world datasets from UCI and KEEL repository, demonstrated that the proposed MLM-KHNN classifier achieves lower classification error rate and is less sensitive to the parameter k, when compared to nine related competitive KNN-based classifiers, especially in small training sample size situations.
The story is based on two real families.
Section Evaluation reports the evaluation on two real-world applications.
We testify the proposed approach on two real-world microarray datasets.
We performed a comprehensive experimental evaluation of Lambda and competing tools on two real-world datasets.
We testify the proposed approach on two real-world microarray datasets of different statistical characteristics.
This study utilized two scenarios for highly and moderately emetogenic chemotherapies: one scenario was based on two clinical studies, 33, 34 the second on a real world clinical setting in Belgium.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com