Stop overkilling simple tasks with black-box models and use transparent models instead

Abstract

In recent years, the employment of deep learning methods has led to several significant breakthroughs in artificial intelligence. Different from traditional machine learning models, deep learning-based approaches are able to extract features autonomously from raw data. This allows for bypassing the feature engineering process, which is generally considered to be both error-prone and tedious. Moreover, deep learning strategies often outperform traditional models in terms of accuracy.

The experimental methodology is lacking. We plan to deeply revise the paper and submit a substantially different version