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