SMOTE
SMOTE: Synthetic Minority Over-sampling Technique. An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ”normal” examples with only a small percentage of ”abnormal” or ”interesting” examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Keywords for this software
References in zbMATH (referenced in 141 articles , 1 standard article )
Showing results 1 to 20 of 141.
Sorted by year (- Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
- Bej, Saptarshi; Davtyan, Narek; Wolfien, Markus; Nassar, Mariam; Wolkenhauer, Olaf: LoRAS: an oversampling approach for imbalanced datasets (2021)
- Cao, Yi; Liu, Xiaoquan; Zhai, Jia: Option valuation under no-arbitrage constraints with neural networks (2021)
- Du, Yu; Lin, Xiaodong; Pham, Minh; Ruszczyński, Andrzej: Selective linearization for multi-block statistical learning (2021)
- Gattermann-Itschert, Theresa; Thonemann, Ulrich W.: How training on multiple time slices improves performance in churn prediction (2021)
- Mao, Shanjun; Fan, Xiaodan; Hu, Jie: Correlation for tree-shaped datasets and its Bayesian estimation (2021)
- Merdan, Selin; Barnett, Christine L.; Denton, Brian T.; Montie, James E.; Miller, David C.: OR practice-data analytics for optimal detection of metastatic prostate cancer (2021)
- Pereira, Rodolfo M.; Costa, Yandre M. G.; Silla, Carlos N. Jr.: Handling imbalance in hierarchical classification problems using local classifiers approaches (2021)
- Saito, Miho; Ohsato, Takaya; Yamanaka, Suguru: An empirical evaluation of machine learning performance in corporate sales growth prediction (2021)
- Shahee, Shaukat Ali; Ananthakumar, Usha: An overlap sensitive neural network for class imbalanced data (2021)
- Soltanzadeh, Paria; Hashemzadeh, Mahdi: RCSMOTE: range-controlled synthetic minority over-sampling technique for handling the class imbalance problem (2021)
- Abdallah, Zahraa S.; Gaber, Mohamed Medhat: Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series (2020)
- Chaabane, Ikram; Guermazi, Radhouane; Hammami, Mohamed: Enhancing techniques for learning decision trees from imbalanced data (2020)
- Gubela, Robin M.; Lessmann, Stefan; Jaroszewicz, Szymon: Response transformation and profit decomposition for revenue uplift modeling (2020)
- Halbersberg, Dan; Wienreb, Maydan; Lerner, Boaz: Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier (2020)
- Lázaro, Marcelino; Herrera, Francisco; Figueiras-Vidal, Aníbal R.: Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification (2020)
- Mahajan, Pravar Dilip; Maurya, Abhinav; Megahed, Aly; Elwany, Alaa; Strong, Ray; Blomberg, Jeanette: Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (2020)
- Ruehle, Fabian: Data science applications to string theory (2020)
- Sun, Hongwei; Cui, Yuehua; Gao, Qian; Wang, Tong: Trimmed LASSO regression estimator for binary response data (2020)
- Tao, Xinmin; Li, Qing; Guo, Wenjie; Ren, Chao; He, Qing; Liu, Rui; Zou, JunRong: Adaptive weighted over-sampling for imbalanced datasets based on density peaks clustering with heuristic filtering (2020)