Cost-Sensitive Support Vector Machines (CSSVM)
This project implements CSSVM using LibSVM.
- Installation
Installing CSSVM is exactly the same as LibSVM:
-
Command line: In the root directory of the project and simply execute
make. -
Python Interface In the
pythondirectory, executemake. -
Run
Running CSSVM is almost like running LibSVM, except the new -C and -W options:
Usage: svm-train [options] training_set_file [model_file]
options:
-s svm_type : set type of SVM (default 0)
0 -- C-SVC (multi-class classification)
1 -- nu-SVC (multi-class classification)
2 -- one-class SVM
3 -- epsilon-SVR (regression)
4 -- nu-SVR (regression)
-t kernel_type : set type of kernel function (default 2)
0 -- linear: u'*v
1 -- polynomial: (gamma*u'*v + coef0)^degree
2 -- radial basis function: exp(-gamma*|u-v|^2)
3 -- sigmoid: tanh(gamma*u'*v + coef0)
4 -- precomputed kernel (kernel values in training_set_file)
-C Cost-sensitive Learning Method(default 0):
0 -- cost-insensitive SVM (biased-penalty SVM with option -w1 C_1 -w-1 C_-1)
1 -- cost-sensitive SVM with option -w1 C_1 -w-1 1/kappa
2 -- cost-sensitive SVM with example-dependent cost.used with option -W cost_file_name
-W cost_file_name : file contains example costs for example-dependent cost-sensitive learning
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/num_features)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 100)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
-v n: n-fold cross validation mode
-q : quiet mode (no outputs)
- CSSVM Tools
Several utiliy functions for training, grid search, and computind different performance measures is provided in cssvm_tools.py.
cssvmutil.py and cssvm.py containt python interface functions for CSSVM.
- Examples
Example scripts involving grid search, cross-validation, train and testing CSSVM is provided for each algorithm:
Cost-Sensitive Learning with Available Class-Dependent Costs (CSA): For the german dataset which its costs are known, run_german perfoms grid search, minimizing the (cost-sensitive) Risk to find the best hyperparameters and threshold for SVM, Biased-Penalty SVM, and CSSVM. The output of the can be found in the run_german.html
Cost-Sensitive Learning with Unavailable Class-Dependent Costs (CSU): (to be added)
Cost-Sensitive Learning with Example-Dependent Costs (CSE): (to be added)
- Cite
@article{masnadi2012cost, title={Cost-Sensitive Support Vector Machines}, author={Masnadi-Shirazi, Hamed and Vasconcelos, Nuno and Iranmehr, Arya}, journal={arXiv preprint arXiv:1212.0975}, year={2012} }
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