Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data
This addresses misclassification issues in pattern recognition for applications with imbalanced data distributions, though it appears incremental as it builds on existing one-class classifier frameworks.
The paper tackles the problem of non-uniformly sampled data degrading one-class classifier performance by proposing OCdmst, a one-class classifier with a dynamic decision boundary based on minimum spanning trees, which achieves state-of-the-art results in comparisons with recent methods.
A typical issue in Pattern Recognition is the non-uniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate predictions. Generally, the data is considered non-uniformly sampled when in a specific area of data space, they are not enough, leading us to misclassification problems. This issue cut down the goal of the one-class classifiers decreasing their performance. In this paper, we propose a one-class classifier based on the minimum spanning tree with a dynamic decision boundary (OCdmst) to make good prediction also in the case we have non-uniformly sampled data. To prove the effectiveness and robustness of our approach we compare with the most recent one-class classifier reaching the state-of-the-art in most of them.