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The data were obtained from the University of California at Irvine (UCI) machine learning repository.
We take the diabetes dataset used in our work from the UCI machine learning repository.
We conducted extensive experiments on 16 multi-class data sets from the UCI machine learning repository.
The benchmark car evaluation dataset from UCI machine learning repository is used for training and testing the SMK.
The online dataset can be made more widely accessible by integrating the dataset with other popular databases, such as the UCI machine learning repository or others.
Additionally, the framework has been evaluated against the open-source Dodgers dataset, available at the UCI machine learning repository, and against the R statistical toolbox.
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Through a numerical experiment using benchmark datasets from the UCI machine-learning repository, we confirm that (i) the suggested node partition method is efficient compared to a random partition method, and (ii) the classification performance of HMC-LAD isuperioror to existing multi-class LAD algorithms and other supervised learning approaches.
The four datasets are available at the UCI Machine learning repository,3 Details about these four datasets may be found in [13].
A series of experiments involving synthetic data and data sets coming from the Machine Learning repository is included as well.
The imbalanced UCI Machine Learning Repository datasets are used in many standard cost sensitive learning papers in literature [2, 3, 10].
The evaluation of the proposed work will also be shown on the open-source Dodgers dataset, available from the UCI Machine Learning repository [22],[22].
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