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Hyperparameter Optimisation for Improving Classification under Class Imbalance

Jiawen Kong; Wojtek Kowalczyk; Duc Anh Nguyen; Stefan Menzel; Thomas Bäck


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    <dcat:keyword>Class Imbalance</dcat:keyword>
    <dcat:keyword>Hyperparameter Optimisation</dcat:keyword>
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    <dct:description>&lt;p&gt;This is the source code used in the paper below:&lt;/p&gt; &lt;p&gt;Jiawen Kong, Wojtek Kowalczyk, Duc Anh Nguyen, Stefan Menzel and Thomas B&amp;auml;ck, &amp;ldquo;Hyperparameter Optimisation for Improving Classification under Class Imbalance&amp;rdquo;, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi:&amp;nbsp;10.1109/SSCI44817.2019.9002679&lt;/p&gt; &lt;p&gt;Although the class-imbalance classification problem has caught a huge amount&amp;nbsp;&lt;br&gt; of attention, hyperparameter optimisation has not been studied in detail in&amp;nbsp;&lt;br&gt; this field. Both classification algorithms and resampling techniques involve&amp;nbsp;&lt;br&gt; some hyperparameters that can be tuned. This paper sets up several&amp;nbsp;&lt;br&gt; experiments and draws the conclusion that, compared to using default&amp;nbsp;&lt;br&gt; hyperparameters, applying hyperparameter optimisation for both&amp;nbsp;&lt;br&gt; classification algorithms and resampling approaches can produce the best&amp;nbsp;&lt;br&gt; results for classifying the imbalanced datasets. Moreover, this paper shows&amp;nbsp;&lt;br&gt; that data complexity, especially the overlap between classes, has a big impact&amp;nbsp;&lt;br&gt; on the potential improvement that can be achieved through hyperparameter&amp;nbsp;&lt;br&gt; optimisation. Results of our experiments also indicate that using resampling&amp;nbsp;&lt;br&gt; techniques cannot improve the performance for some complex datasets, which&amp;nbsp;&lt;br&gt; further emphasizes the importance of analyzing data complexity before dealing&amp;nbsp;&lt;br&gt; with imbalanced datasets.&lt;/p&gt;</dct:description>
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