Packages

p

soul.algorithm

oversampling

package oversampling

Type Members

  1. class ADASYN extends AnyRef

    ADASYN algorithm.

    ADASYN algorithm. Original paper: "ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning" by Haibo He, Yang Bai, Edwardo A. Garcia, and Shutao Li.

  2. class ADOMS extends AnyRef

    ADOMS algorithm.

    ADOMS algorithm. Original paper: "The Generation Mechanism of Synthetic Minority Class Examples" by Sheng TANG and Si-ping CHEN.

  3. class BorderlineSMOTE extends AnyRef

    Borderline-SMOTE algorithm.

    Borderline-SMOTE algorithm. Original paper: "Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning." by Hui Han, Wen-Yuan Wang, and Bing-Huan Mao.

  4. class DBSMOTE extends AnyRef

    DBSMOTE algorithm.

    DBSMOTE algorithm. Original paper: "DBSMOTE: Density-Based Synthetic Minority Over-sampling Technique" by Chumphol Bunkhumpornpat, Krung Sinapiromsaran and Chidchanok Lursinsap.

  5. class MDO extends AnyRef

    MDO algorithm.

    MDO algorithm. Original paper: "To combat multi-class imbalanced problems by means of over-sampling and boosting techniques" by Lida Adbi and Sattar Hashemi.

  6. class MWMOTE extends AnyRef

    MWMOTE algorithm.

    MWMOTE algorithm. Original paper: "MWMOTE—Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning" by Sukarna Barua, Md. Monirul Islam, Xin Yao, Fellow, IEEE, and Kazuyuki Muras.

  7. class RO extends AnyRef

    Random Oversampling algorithm.

    Random Oversampling algorithm. Original paper: "A study of the behavior of several methods for balancing machine learning training data" by Batista, Gustavo EAPA and Prati, Ronaldo C and Monard, Maria Carolina.

  8. class SMOTE extends AnyRef

    SMOTE algorithm.

    SMOTE algorithm. Original paper: "SMOTE: Synthetic Minority Over-sampling Technique" by Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall and W. Philip Kegelmeyer.

  9. class SMOTEENN extends AnyRef

    SMOTEENN algorithm.

    SMOTEENN algorithm. Original paper: "A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data" by Gustavo E. A. P. A. Batista, Ronaldo C. Prati and Maria Carolina Monard.

  10. class SMOTERSB extends AnyRef

    SMOTERSB algorithm.

    SMOTERSB algorithm. Original paper: "kNN Approach to Unbalanced Data Distribution: SMOTE-RSB: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory" by Enislay Ramentol, Yailé Caballero, Rafael Bello and Francisco Herrera.

  11. class SMOTETL extends AnyRef

    SMOTETL algorithm.

    SMOTETL algorithm. Original paper: "A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data" by Gustavo E. A. P. A. Batista, Ronaldo C. Prati and Maria Carolina Monard.

  12. class SafeLevelSMOTE extends AnyRef

    SafeLevel-SMOTE algorithm.

    SafeLevel-SMOTE algorithm. Original paper: "Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling Technique for Handling the Class Imbalanced Problem" by Chumphol Bunkhumpornpat, Krung Sinapiromsaran, and Chidchanok Lursinsap.

  13. class Spider2 extends AnyRef

    Spider2 algorithm.

    Spider2 algorithm. Original paper: "Learning from Imbalanced Data in Presence of Noisy and Borderline Examples" by Krystyna Napiera la, Jerzy Stefanowski and Szymon Wilk.

Ungrouped