KR2023Proceedings of the 20th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning

Rhodes, Greece. September 2-8, 2023.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-02-7

Sponsored by
Published by

Copyright © 2023 International Joint Conferences on Artificial Intelligence Organization

Diagnosis for Post Concept Drift Decision Trees Repair

  1. Shaked Almog(Ben Gurion University)
  2. Meir Kalech(Ben Gurion University)


  1. Applications of KR in diagnosis
  2. Applications that combine KR with machine learning
  3. Explainable AI
  4. Explanation finding, diagnosis, causal reasoning, abduction


Decision trees are commonly used in machine learning since they are accurate and robust classifiers. After a decision tree is built, the data can change over time, causing the classification performance to decrease. This data distribution change is a known challenge in machine learning, referred to as concept drift. Once a concept drift has been detected, usually by experiencing a decrease in the model's performance, it can be handled by training a new model.

However, this method does not explain the drift harming the performance but only handles the drift's effects.

The main contribution of this paper presents a novel two-step approach called APPETITE, which applies diagnosis techniques to identify the feature that has drifted and then adjusts the model accordingly. For the diagnosis step, we present two algorithms. We experimented on 73 known datasets from the literature and semi-synthesized drifts in their features. Both algorithms are better at handling concept drift than training a new model based on the samples after the drift. Combining the two algorithms can provide an explanation of the drift and is a competitive model against a new model trained on the entire data from before and after the drift.