Combining deep ensemble learning and explanation for intelligent ticket

Intelligent Ticket Management Systems, equipped with automated ticket classification tools, are an advanced solution for handling customer-support activities. Some recent approaches to ticket classification leverage Deep Learning (DL) methods, in place of traditional ones using standard Machine Learning and feature engineering techniques. However, two challenging objectives should be addressed when applying DL methods to real-life contexts: (i) curbing the risk of having an overfitting model that hinges on spurious ticket features, and (ii) trying to explain the ticket classifications returned by such black-box models. In this work, we propose a comprehensive ticket classification framework, which relies on training a novel kind of ensemble of deep classifiers, and on providing AI-based interpretation methods to help both the operator in recognizing misclassification errors and the analyst in improving and fine-tuning the model. Tests on real data confirmed the accuracy of the classifications returned by the framework, and the practical value of their associated explanations.

Automated summary

The study proposes a comprehensive ticket classification framework that leverages deep learning methods and AI-based interpretation to address overfitting and black-box model interpretation challenges in ticket classifications. Tests on real data demonstrated the accuracy of classifications and the practical value of associated explanations.