Why does rebalancing class unbalanced data improve AUC for linear discriminant analysis?
In particular, using the rebalanced training data can often improve the area under the receiver operating characteristic curve (AUC) for the original, unbalanced test data. We show that the largest improvement of AUC is achieved, asymptotically, when the two classes are fully rebalanced to be of equal sizes.
What is class imbalance problem in data set?
Definition. Data are said to suffer the Class Imbalance Problem when the class distributions are highly imbalanced. In this context, many classification learning algorithms have low predictive accuracy for the infrequent class. Cost-sensitive learning is a common approach to solve this problem.
Do unbalanced data have a negative effect on LDA?
1. Introduction. For two-class discrimination, Xie and Qiu [1] claims that, when covariance matrices of the two classes are unequal, a (class) unbalanced data set has a negative effect on the performance of linear discriminant analysis (LDA).
Do we need validation set?
Validation set is different from test set. Validation set actually can be regarded as a part of training set, because it is used to build your model, neural networks or others. It is usually used for parameter selection and to avoild overfitting. Validation set is used for tuning the parameters of a model.
How to deal with an imbalanced training set?
The idea of balancing the training set + validating the balancing method is for being able to generalize your model that is would discriminate (in classification assignment) better a sample from the minority class, in an unseen and imbalanced test set.
How to address the problem of class imbalance?
One approach to addressing the problem of class imbalance is to randomly resample the training dataset. The two main approaches to randomly resampling an imbalanced dataset are to delete examples from the majority class, called undersampling, and to duplicate examples from the minority class, called oversampling.
How to deal with a class imbalance dataset?
If you are sampling randomly for the training and testing, then the ratio is still 90:10 in the testing set. If your model is very biased , that predicts all the samples to be class A , then: Overall accuracy = 90% Average accuracy = 50 % ( 100% for class A + 0% for class B) / 2
How is random resampling used for imbalanced classification?
There are two main approaches to random resampling for imbalanced classification; they are oversampling and undersampling. Random Oversampling: Randomly duplicate examples in the minority class. Random Undersampling: Randomly delete examples in the majority class.