The KPBoost-SVM method can be used to effectively perform boosting with SVMs. SVMs are stable learner which cannot be readily boosted by resampling/reweighting the data. Therefore, KPBoost-SVM uses Kernel Perturbation to boost SVMs. The idea is to increase, in each round of boosting, the resolution of the RBF-kernel-induced feature space around the data points which are misclassified in the previous round. The resolution is increased by using a conformal transformation. The KPBoostROI-SVM method is a variant of KPBoost-SVM which calculates the resolution around the individual test points, unlike KPBoost-SVM (which assumes maximum possible resolution around all test data points).
This code bundle contains codes for IDENTIFYING the DISJUNCTS in a DATASET (findDisjunts.m) and also contains an implementation of the Geometric Small Disjunct Index for MEASURING the PERFORMANCE on the SMALL DISJUNCTS for ANY CLASSIFIER (GSDI.m).
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- Load the required data (see 'sampleWorkspace.mat' for an example) in the MATLAB workspace.
- Run 'KPBoostCode.m' with appropriate choice of parameters (see our article on arXiv for recommended settings: https://arxiv.org/abs/1712.08493).
- To only identify the disjuncts in a dataset, use the function 'findDisjuncts.m'.
- To only measure the performance on the small disjuncts for a given classification, use the function 'GSDI.m'.