How to correct data imbalance in SVM using LIBSVM?
The two most popular approaches are: Use different misclassification penalties per class, this basically means changing C. Typically the smallest class gets weighed higher, a common approach is npos * wpos = nneg * wneg. LIBSVM allows you to do this using its -wX flags.
How to classify multiple classes in LIBSVM format?
They are in the original format instead of the libsvm format: in each row the 2nd value gives the class label and subsequent numbers give pairs of feature IDs and values. We then do a kind of tf-idf transformation: ln (1+tf)*log_2 (#docs/#coll_freq_of_term) and normalize each instance to unit length.
Where can I find data in LIBSVM format?
This page contains many classification, regression, multi-label and string data sets stored in LIBSVM format. For some sets raw materials (e.g., original texts) are also available. These data sets are from UCI, Statlog, StatLib and other collections.
How to split training and testing in LIBSVM?
We select train-0.tc and test-0.tc from ecoc-svm-data.tar.gz. A 2/1 training/testing split gives training and testing sets below. They are in the original format instead of the libsvm format: in each row the 2nd value gives the class label and subsequent numbers give pairs of feature IDs and values.
How to work with multi class problem in LIBSVM?
You dont need to do anything special to work with multiclass problem in LibSVM. Just give the proper label to each instance (1, 2., n). Internally, LibSVM will perform a “one against one” problem for each two class. It means that for each two class, an SVM will be trained.
How to set gamma and cost parameters in LIBSVM?
How should I set my gamma and Cost parameters in libSVM when I am using an imbalanced dataset that consists of 75% ‘true’ labels and 25% ‘false’ labels? I’m getting a constant error of having all the predicted labels set on ‘True’ due to the data imbalance.
How to create a multi class SVM problem?
For a k-class data, internally labels are 0., k-1, and each two-class SVM considers pair (i, j) with i < j. Then class i is treated as positive (+1) and j as negative (-1). For example, if the data set has labels +5/+10 and +10 appears first, then internally the +5 versus +10 SVM problem has +10 as positive (+1) and +5 as negative (-1).”