Imbalanced classification with tpg genetic programming

Author:

Sourbier Nicolas1,Bonnot Justine1,Majorczyk Frédéric2,Gesny Olivier3,Guyet Thomas4,Pelcat Maxime1

Affiliation:

1. Univ Rennes, Rennes, France

2. DGA-MI - CIDRE, Bruz, France

3. Silicom, Cesson-Sevigné, France

4. Inria - Centre de Lyon, France

Publisher

ACM

Reference29 articles.

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2. Anomaly detection

3. N. V Chawla , K. W Bowyer , L. O. Hall , and W P. Kegelmeyer . Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research , 2002 . N. V Chawla, K. W Bowyer, L. O. Hall, and W P. Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 2002.

4. D. Chicco and G. Jurman . The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics , 2020 . D. Chicco and G. Jurman. The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics, 2020.

5. The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

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1. Explainable Artificial Intelligence by Genetic Programming: A Survey;IEEE Transactions on Evolutionary Computation;2022

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