More Interpretable Decision Trees

• Eugene Gilmore ^[1] ; Vladimir Estivill-Castro ^[2] ; René Hexel ^[1]
  1. [1] Griffith University

     Griffith University

     Australia

     
  2. [2] Universitat Pompeu Fabra

     Universitat Pompeu Fabra

     Barcelona, España

     
• Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López, Pablo García Bringas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2021, ISBN 978-3-030-86271-8, págs. 280-292
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • We present a new Decision Tree Classifier (DTC) induction algorithm that produces vastly more interpretable trees in many situations. These understandable trees are highly relevant for explainable artificial intelligence, fair automatic classification, and human-in-the-loop learning systems. Our method is an improvement over the Nested Cavities (NC) algorithm. That is, we profit from the parallel-coordinates visualisation of high dimensional datasets. However, we build a hybrid with other decision tree heuristics to generate node-expanding splits. The rules in the DTCs learnt using our algorithm have a straightforward representation and, thus, are readily understood by a human user, even though our algorithm constructs rules whose nodes can involve multiple attributes. We compare our algorithm to the well-known decision tree induction algorithm C4.5, and find that our methods produce similar accuracy with significantly smaller trees. When coupled with a humanin-the-loop-learning (HILL) system, our approach can be highly effective for inferring understandable patterns in datasets.