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The diversification (generating slightly varying separating discriminators)of Support Vector Machines (SVMs) for boosting has proven to be a challenge dueto the strong learning nature of SVMs. Based on the insight that perturbing theSVM kernel may help in diversifying SVMs, we propose two kernel perturbationbased boosting schemes where the kernel is modified in each round so as toincrease the resolution of the kernel-induced Reimannian metric in the vicinityof the datapoints misclassified in the previous round. We propose a method foridentifying the disjuncts in a dataset, dispelling the dependence on rule-basedlearning methods for identifying the disjuncts. We also present a newperformance measure called Geometric Small Disjunct Index (GSDI) to quantifythe performance on small disjuncts for balanced as well as class imbalanceddatasets. Experimental comparison with a variety of state-of-the-art algorithmsis carried out using the best classifiers of each type selected by a newapproach inspired by multi-criteria decision making. The proposed method isfound to outperform the contending state-of-the-art methods on differentdatasets (ranging from mildly imbalanced to highly imbalanced and characterizedby varying number of disjuncts) in terms of three different performance indices(including the proposed GSDI).

Diversifying Support Vector Machines for Boosting using Kernel Perturbation: Applications to Class Imbalance and Small Disjuncts