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An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version).

, , , , , , and . CoRR, (2020)

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A meta-learning approach for selecting image segmentation algorithm., , , , , and . Pattern Recognit. Lett., (2019)Time-Series in Hyper-parameter Initialization of Machine Learning Techniques., , and . IDEAL, volume 13113 of Lecture Notes in Computer Science, page 246-258. Springer, (2021)Exploring Autoencoders for Feature Extraction in Multi-Target Classification., , and . IJCNN, page 1-8. IEEE, (2021)Effects of Random Sampling on SVM Hyper-parameter Tuning., , and . ISDA, volume 557 of Advances in Intelligent Systems and Computing, page 268-278. Springer, (2016)AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification., , and . BRACIS (2), volume 14196 of Lecture Notes in Computer Science, page 291-306. Springer, (2023)A Meta-Learning Approach for Recommendation of Image Segmentation Algorithms., , and . SIBGRAPI, page 370-377. IEEE Computer Society, (2016)An empirical study on hyperparameter tuning of decision trees., , , , , and . CoRR, (2018)Transfer Learning for Algorithm Recommendation., , , , and . CoRR, (2019)To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning., , , , and . IJCNN, page 1-8. IEEE, (2015)Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms., , and . CBMS, page 50-53. IEEE Computer Society, (2015)