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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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  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3855193", 
  "language": "eng", 
  "title": "Hyperparameter Optimisation for Improving Classification under Class Imbalance", 
  "issued": {
    "date-parts": [
  "abstract": "<p>This is the source code used in the paper below:</p>\n\n<p>Jiawen Kong, Wojtek Kowalczyk, Duc Anh Nguyen, Stefan Menzel and Thomas B&auml;ck, &ldquo;Hyperparameter Optimisation for Improving Classification under Class Imbalance&rdquo;, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi:&nbsp;10.1109/SSCI44817.2019.9002679</p>\n\n<p>Although the class-imbalance classification problem has caught a huge amount&nbsp;<br>\nof attention, hyperparameter optimisation has not been studied in detail in&nbsp;<br>\nthis field. Both classification algorithms and resampling techniques involve&nbsp;<br>\nsome hyperparameters that can be tuned. This paper sets up several&nbsp;<br>\nexperiments and draws the conclusion that, compared to using default&nbsp;<br>\nhyperparameters, applying hyperparameter optimisation for both&nbsp;<br>\nclassification algorithms and resampling approaches can produce the best&nbsp;<br>\nresults for classifying the imbalanced datasets. Moreover, this paper shows&nbsp;<br>\nthat data complexity, especially the overlap between classes, has a big impact&nbsp;<br>\non the potential improvement that can be achieved through hyperparameter&nbsp;<br>\noptimisation. Results of our experiments also indicate that using resampling&nbsp;<br>\ntechniques cannot improve the performance for some complex datasets, which&nbsp;<br>\nfurther emphasizes the importance of analyzing data complexity before dealing&nbsp;<br>\nwith imbalanced datasets.</p>", 
  "author": [
      "family": "Jiawen Kong"
      "family": "Wojtek Kowalczyk"
      "family": "Duc Anh Nguyen"
      "family": "Stefan Menzel"
      "family": "Thomas B\u00e4ck"
  "id": "3855193", 
  "event-place": "Xiamen, China", 
  "type": "article", 
  "event": "The 2019 IEEE Symposium Series on Computational Intelligence (SSCI)"
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