Process mining classification with a weightless neural network

TL;DR

This paper introduces a novel graph encoding method for process mining using WiSARD weightless neural networks, demonstrating improved classification performance with limited training data.

Contribution

It proposes a new graph encoding technique and applies WiSARD neural networks to enhance process classification in process mining.

Findings

WiSARD outperforms traditional classifiers with small training sets

A new graph to retina codification effectively represents process flows

The approach simplifies process classification tasks

Abstract

Using a weightless neural network architecture WiSARD we propose a straightforward graph to retina codification to represent business process graph flows avoiding kernels, and we present how WiSARD outperforms the classification performance with small training sets in the process mining context.