Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset
暂无分享,去创建一个
Sung Wook Baik | Mi Young Lee | Tuong Le | Mi Young Lee | Jun Ryeol Park | S. Baik | Tuong Le | J. Park
[1] Zhenyu He,et al. A multi-view model for visual tracking via correlation filters , 2016, Knowl. Based Syst..
[2] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[3] Yuri Zelenkov,et al. Two-step classification method based on genetic algorithm for bankruptcy forecasting , 2017, Expert Syst. Appl..
[4] Fernando Nogueira,et al. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning , 2016, J. Mach. Learn. Res..
[5] Sashank Dara,et al. Online Defect Prediction for Imbalanced Data , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[6] Xiao Liu,et al. Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data , 2016, Knowl. Based Syst..
[7] Xiong Xiong,et al. The effect of genetic algorithm learning with a classifier system in limit order markets , 2017, Eng. Appl. Artif. Intell..
[8] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[9] Bay Vo,et al. Mining top-k co-occurrence items with sequential pattern , 2017, Expert Syst. Appl..
[10] Jing He,et al. A Classifier Hub for Imbalanced Financial Data , 2016, ADC.
[11] Dae-Ki Kang,et al. Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction , 2015, Expert Syst. Appl..
[12] Tzung-Pei Hong,et al. Efficient Algorithms for Mining Erasable Closed Patterns From Product Datasets , 2017, IEEE Access.
[13] Ekrem Duman,et al. A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing , 2016, Neurocomputing.
[14] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[15] Francesco Sergio Pisani,et al. An Incremental Ensemble Evolved by using Genetic Programming to Efficiently Detect Drifts in Cyber Security Datasets , 2016, GECCO.
[16] Wojtek Michalowski,et al. Application of Preprocessing Methods to Imbalanced Clinical Data: An Experimental Study , 2016, ITIB.
[17] Jun Li,et al. Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction , 2017, Eng. Appl. Artif. Intell..
[18] Bay Vo,et al. A novel approach for mining maximal frequent patterns , 2017, Expert Syst. Appl..
[19] Sung Wook Baik,et al. Efficient algorithms for mining top-rank-k erasable patterns using pruning strategies and the subsume concept , 2018, Eng. Appl. Artif. Intell..
[20] Kyung-shik Shin,et al. Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction , 2016, Expert Syst. Appl..
[21] Le Hoang Son,et al. Novel fuzzy clustering scheme for 3D wireless sensor networks , 2017, Appl. Soft Comput..
[22] Jie Zhang,et al. A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces , 2017, Sensors.
[23] Mumtaz Ali,et al. A Novel Clustering Algorithm in a Neutrosophic Recommender System for Medical Diagnosis , 2017, Cognitive Computation.
[24] Qionghai Dai,et al. ACID: Association Correction for Imbalanced Data in GWAS , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[25] 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).
[26] Le Hoang Son,et al. Linguistic Vector Similarity Measures and Applications to Linguistic Information Classification , 2017, Int. J. Intell. Syst..
[27] Sungzoon Cho,et al. EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems , 2006, ICONIP.
[28] Bay Vo,et al. The lattice‐based approaches for mining association rules: a review , 2016, WIREs Data Mining Knowl. Discov..
[29] Herbert Kimura,et al. Machine learning models and bankruptcy prediction , 2017, Expert Syst. Appl..
[30] Witold Pedrycz,et al. Mining erasable itemsets with subset and superset itemset constraints , 2017, Expert Syst. Appl..
[31] Huiling Chen,et al. An Effective Computational Model for Bankruptcy Prediction Using Kernel Extreme Learning Machine Approach , 2017 .
[32] Jakub M. Tomczak,et al. Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction , 2016, Expert Syst. Appl..
[33] Le Hoang Son,et al. Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences , 2016, Applied Intelligence.
[34] Jorma Laurikkala,et al. Improving Identification of Difficult Small Classes by Balancing Class Distribution , 2001, AIME.
[35] Rahul Bhattacharyya,et al. Air filter particulate loading detection using smartphone audio and optimized ensemble classification , 2017, Eng. Appl. Artif. Intell..
[36] Hui Han,et al. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.
[37] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.
[38] W. Pietruszkiewicz,et al. Dynamical systems and nonlinear Kalman filtering applied in classification , 2008, 2008 7th IEEE International Conference on Cybernetic Intelligent Systems.