A Generalized Framework with Adaptive Weighted Soft-Margin for
  Imbalanced SVM Classification
Lu Jiang Qi Wang Yuhang Chang Jianing Song Haoyue Fu
Category imbalance is one of the most popular and important issues in the
domain of classification. In this paper, we present a new generalized framework
with Adaptive Weight function for soft-margin Weighted SVM (AW-WSVM), which
aims to enhance the issue of imbalance and outlier sensitivity in standard
support vector machine (SVM) for classifying two-class data. The weight
coefficient is introduced into the unconstrained soft-margin support vector
machines, and the sample weights are updated before each training. The Adaptive
Weight function (AW function) is constructed from the distance between the
samples and the decision hyperplane, assigning different weights to each
sample. A weight update method is proposed, taking into account the proximity
of the support vectors to the decision hyperplane. Before training, the weights
of the corresponding samples are initialized according to different categories.
Subsequently, the samples close to the decision hyperplane are identified and
assigned more weights. At the same time, lower weights are assigned to samples
that are far from the decision hyperplane. Furthermore, we also put forward an
effective way to eliminate noise. To evaluate the strength of the proposed
generalized framework, we conducted experiments on standard datasets and
emotion classification datasets with different imbalanced ratios (IR). The
experimental results prove that the proposed generalized framework outperforms
in terms of accuracy, recall metrics and G-mean, validating the effectiveness
of the weighted strategy provided in this paper in enhancing support vector
machines.