Hyperparameter Optimisation for Improving Classification under Class Imbalance
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Thomas Bäck | Stefan Menzel | Wojtek Kowalczyk | Jiawen Kong | Duc Anh Nguyen | Thomas Bäck | W. Kowalczyk | S. Menzel | Jiawen Kong | D. Nguyen
[1] Yoshua Bengio,et al. Algorithms for Hyper-Parameter Optimization , 2011, NIPS.
[2] Mark Johnston,et al. Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data , 2013, IEEE Transactions on Evolutionary Computation.
[3] Francisco Herrera,et al. Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics , 2012, Expert Syst. Appl..
[4] Francisco Herrera,et al. An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics , 2013, Inf. Sci..
[5] I. Tomek,et al. Two Modifications of CNN , 1976 .
[6] José Martínez Sotoca,et al. Data Characterization for Effective Prototype Selection , 2005, IbPRIA.
[7] Chris Eliasmith,et al. Hyperopt: a Python library for model selection and hyperparameter optimization , 2015 .
[8] Seetha Hari,et al. Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.
[9] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.
[10] Gustavo E. A. P. A. Batista,et al. Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior , 2004, MICAI.
[11] Haibo He,et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[12] Charles X. Ling,et al. Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.
[13] Vaishali Ganganwar,et al. An overview of classification algorithms for imbalanced datasets , 2012 .
[14] Miriam Seoane Santos,et al. Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier] , 2018, IEEE Computational Intelligence Magazine.
[15] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[16] Lars Schmidt-Thieme,et al. Improving Academic Performance Prediction by Dealing with Class Imbalance , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.
[17] R. Barandelaa,et al. Strategies for learning in class imbalance problems , 2003, Pattern Recognit..
[18] Jens Lehmann,et al. How Complex Is Your Classification Problem? , 2018, ACM Comput. Surv..
[19] Tin Kam Ho,et al. Complexity Measures of Supervised Classification Problems , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[20] David D. Cox,et al. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures , 2013, ICML.
[21] Rok Blagus,et al. Joint use of over- and under-sampling techniques and cross-validation for the development and assessment of prediction models , 2015, BMC Bioinformatics.
[22] María José del Jesús,et al. KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..