Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation

TL;DR

This paper introduces an improved graph classifier that leverages dissimilarity representation and Re9nyi entropy, achieving high accuracy and faster computation on labeled graph datasets.

Contribution

It presents a novel enhancement of a general-purpose graph classifier by optimizing data compression using information-theoretic techniques and evolutionary algorithms.

Findings

Achieves state-of-the-art accuracy on benchmark datasets.

Significantly reduces computational time.

Produces more parsimonious classification models.

Abstract

Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various datasets of labeled graphs. However, the design of effective learning procedures operating in the space of labeled graphs is still a challenging problem, especially from the computational complexity viewpoint. In this paper, we present a major improvement of a general-purpose classifier for graphs, which is conceived on an interplay between dissimilarity representation, clustering, information-theoretic techniques, and evolutionary optimization algorithms. The improvement focuses on a specific key subroutine devised to compress the input data. We prove different theorems which are fundamental to the setting of the parameters controlling such a compression operation. We demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well-known datasets of labeled graphs, considering as distinct performance indicators the classification accuracy, computing time, and parsimony in terms of structural complexity of the synthesized classification models. The results show state-of-the-art standards in terms of test set accuracy and a considerable speed-up for what concerns the computing time.