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Paper: Computational Resource Optimisation in Feature Selection under Class Imbalance Conditions #947
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Thank you @apaleyes for your insightful comments that has enhance the content and quality of the paper. We have provided a more unified conclusion that clarifies that the paper is a preliminary study that considers five datasets with substantial sample size, characterised by class imbalance. A justification for the selection of the models has been included in the text. This informed the choice of PFI for the feature selection process owing to its advantage of being model-agnostic. Expansion to include other models and much larger datasets has been included in the conclusion for further study.
Thank you @apaleyes for the insightful comments provided to this paper, which has enhanced the quality and richness of the paper. |
Thank you @janeadams for the observations and review of the paper. This has provided clarity to aspects of the data visualisation and improved the deductions on the model performance. An explanation for SVM’s poor performance included in the text. |
Lovely, thanks for all the work on updating the paper @AmadiGabriel ! I'll have another look shortly |
"shortly" ha-ha (one month later) Anyhow, @cbcunc @ameyxd I am happy with the changes made to the paper and how the comments were addressed. If I am reading it right some additional experiments were run, quite impressive! To the authors, I still think the number of features in the datasets reviewed isn't big enough to justify feature selection. It absolutely works as the first step, but would be nice to see the follow up work on larger datasets, as the future work paragraph promises. Same conference next year? |
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