# Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
## 2 Related Work
In the second case, a cost-sensitive multi-label learning is made up of two different classification approaches, such as binary-class imbalance classifier and multi-class imbalance classifier. To handle the problem about class imbalance and concept drift in multi-label stream classification, [PERSON] et al. [25] used a multiple window method. By combing labels, [PERSON] et al. [26] proposed a multi-label learning method called DEML (Dealing with labels imbalance by Entropy for Multi-Label classification). To leverage the exploration of class imbalance and the exploitation of label correlation, a multi-label learning approach called Cross-Coupling Aggregation (COCOA) [7] has also been proposed. Although the effectiveness of COCOA has been validated, the class imbalance may still affect a multi-class imbalance learner when the number of a coupling label is too small. To handle class-imbalanced training data, many multi-class approaches have been developed. In general, the existing approaches can be categorized as data-adaption approaches and algorithmic-adaption approaches [27; 28; 29]. In data-adaption approaches, the minority class examples and majority class examples are balanced by sampling strategies, e.g., under-sampling or over-sampling. The over-sampling process creates synthetic examples corresponding to minority examples, whereas the under-sampling process reduces the number of majority examples. To create synthetic examples, some techniques apply random pattern, while others follow density distribution [30]. Algorithmic-adaption approaches involve approaches that adapt to imbalanced data. For example, cost-sensitive learning approaches spend higher cost in learning minority class [31]. Boosting methods integrate sampling and algorithmic-adaption approaches to deal with class-imbalanced data sets. AdaBoost [32] was developed to sequentially learn multiple classifiers and integrate them to achieve better performance by minimizing an error function. AdaBoost can not only be used to one-class classification but also multi-class classification. AdaBoost is able to be directly applied to multiple binary classifications transformed by multi-class classification, e.g., AdaBoost.M2 [32] and AdaBoost.MH [33]. In these approaches, higher costs and extended training time are required to learn many weak classifiers, and the accuracy will be limited if the number of classes are large. AdaBoost.M1 directly generalizes AdaBoost into multi-class classification, but it requires the accuracy of each weak classifier larger than a strict error bound. Stage-wise Additive Modeling using Multi-class using Multi-class exponential (SAMME) loss function [34] has been used to extend AdaBoost methods to multi-class classification. SAMME eases the accuracy of each weak classifier in AdaBoost.M1 from 1/2 to 1/k so that the weak classifier whose performance is better than random guesses is accepted. However, these multi-class boosting approaches neglect the deterioration of classification accuracy in the training process. A regularized ensemble framework [8] was therefore introduced to learn multi-class imbalanced data sets. To adapt multi-class imbalanced data sets, a regularization term is applied to automatically adjust every classifier's error bound according to its performance. Furthermore, the regularization term will penalize the classifier if it incorrectly classifies examples that had been classified correctly by the previous classifier.