One-class classifiers based on entropic spanning graphs

TL;DR

This paper introduces a novel one-class classifier based on entropic spanning graphs that effectively handles both numeric and non-numeric data, capturing complex geometric structures and providing confidence measures.

Contribution

The paper proposes a new entropic spanning graph-based methodology for one-class classification, including a mutual information criterion and a graph-based fuzzy model for confidence estimation.

Findings

Effective on benchmark datasets with feature vectors and graphs

Versatile in handling complex geometric data structures

Outperforms state-of-the-art methods in experiments

Abstract

One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach takes into account the possibility to process also non-numeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data and the outcoming partition of vertices defines the classifier. The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the $\alpha$-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for data characterized by complex geometric structures. We provide experiments on well-known benchmarks containing both feature vectors and labeled graphs. In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches.