How do you handle data imbalance in SVM? - Stack Overflow My experience is that standard SVM classifiers do not really work nicely on unbalanced data I encountered that for the C-SVM and it is even worse for the nu-SVM Maybe you want to have a look at P-SVM which offers a mode that is especially suitable for unbalanced data
Cost-Sensitive SVM for Imbalanced Classification This modification of SVM that weighs the margin proportional to the class importance is often referred to as weighted SVM, or cost-sensitive SVM In this tutorial, you will discover weighted support vector machines for imbalanced classification
SVM: Separating hyperplane for unbalanced classes SVM: Separating hyperplane for unbalanced classes # Find the optimal separating hyperplane using an SVC for classes that are unbalanced We first find the separating plane with a plain SVC and then plot (dashed) the separating hyperplane with automatically correction for unbalanced classes
Methods for class-imbalanced learning with support vector machines: a . . . We first explain the structure of SVM and its variants and discuss their inefficiency in learning with class-imbalanced data sets We introduce a hierarchical categorization of SVM-based models with respect to class-imbalanced learning
libsvm - SVM for unbalanced data - Cross Validated Before I attempt the problem though, I was warned that SVMs dont perform well on extremely unbalanced data In my case, I can have as much as 95-98% 0's and 2-5% 1's