Table 1 Overview of the boosting approaches
From: Boosting methods for multi-class imbalanced data classification: an experimental review
Approach | Brief description | Years |
|---|---|---|
AdaBoost.M1 [43] | A multiclass variation of AdaBoost which uses multiclass base classifier Weight of each base classifiers is a function of error rate | 1997 |
AdaBoost.M2 [43] | A multiclass variation of AdaBoost Weight of each base classifiers is a function of pseudo-loss | 1997 |
GentleBoost [45] | Extended version for AdaBoost which uses Newton steps Using weighted least-squares regression for fitting the base classifiers | 2000 |
CSB1 [58] | A Cost-sensitive variation of AdaBoost proposed for handling imbalanced data Adding cost item into the weight update formula of AdaBoost Removing step size coefficient from the weight update formula of AdaBoost | 2000 |
CSB2 [58] | A Cost-sensitive variation of AdaBoost proposed for handling imbalanced data Adding cost item into the weight update formula of AdaBoost The step size is considered in the weight update formula, like AdaBoost | 2000 |
MAdaBoost [59] | Proposed with the goal of solving the AdaBoost's sensitivity to noise Modifying the weight update formula of AdaBoost | 2000 |
An improvement for AdaBoost Using different weight update scheme for positive and negative predictions Considering False Positive, True Positive, True Negative and False Negative in step size calculation | 2001 | |
Modest AdaBoost [62] | An improvement of GentleBoost Using different weight update formula for misclassified and truly classified samples Using inverted distribution to assign larger weights to truly classified samples | 2005 |
JOUSBoost [63] | Proposed with the goal of handling imbalanced data in AdaBoost algorithm Combining the jittering of the data and sampling techniques with AdaBoost | 2007 |
ABC-LogitBoost [47] | An improvement of LogitBoost for multiclass classification Solving the difficulties of dense Hessian Matrix in Logistic loss | 2009 |
AdaBoost.HM [64] | A multiclass variation of AdaBoost which uses hypothesis margin Using multiclass base classifiers instead of decomposing the multiclass classification problem into multiple binary problems | 2010 |
RAMOBoost [65] | Proposed with the goal of imbalanced data handling Combining Ranked Minority Oversampling with AdaBoost.M2 Using the sampling probability distribution for ranking the minority class samples | 2010 |
AOSO-LogitBoost [48] | One versus one version of LogitBoost for multiclass classification Solving the difficulties of dense Hessian Matrix in Logistic loss by utilizing vector tree and adaptive block coordinate descent techniques | 2011 |
CD-MCBoost [66] | Performing coordinate descent on multiclass loss function Concentration of each base classifier on margin maximization of a single class | 2011 |
EUSBoost [67] | An improvement of RUSBoost which uses evolutionary undersampling Using different subsets of majority class samples in the training phase of each base classifier to ensure diversity | 2013 |
RB-Boost [68] | Combining Random Balance with AdaBoost.M2 Using SMOTE sampling to deal with imbalanced data problem The difference with SMOTEBoost is using random proportion of classes in each iteration of booting to ensure the diversity of base classifiers | 2015 |
LIUBoost [69] | Proposed with the goal of imbalanced data handling Using undersampling in order to solve the imbalanced data problem Adding a cost term to the weight update formula of the samples | 2019 |
TLUSBoost [70] | Proposed with the goal of imbalanced data handling Using Tomek-linked and redundancy-based undersampling for removing outlier samples | 2019 |