Diagnosis for Post Concept Drift Decision Trees Repair

Shaked Almog, Meir Kalech

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning, KR 2023
EditorsPierre Marquis, Tran Cao Son, Gabriele Kern-Isberner
PublisherAssociation for the Advancement of Artificial Intelligence
Pages23-33
Number of pages11
ISBN (Electronic)9781956792027
StatePublished - 1 Jan 2023
Event20th International Conference on Principles of Knowledge Representation and Reasoning, KR 2023 - Rhodes, Greece
Duration: 2 Sep 20238 Sep 2023

Publication series

NameProceedings of the International Conference on Knowledge Representation and Reasoning
ISSN (Print)2334-1025
ISSN (Electronic)2334-1033

Conference

Conference20th International Conference on Principles of Knowledge Representation and Reasoning, KR 2023
Country/TerritoryGreece
CityRhodes
Period2/09/238/09/23

ASJC Scopus subject areas

  • Software
  • Logic

Fingerprint

Dive into the research topics of 'Diagnosis for Post Concept Drift Decision Trees Repair'. Together they form a unique fingerprint.

Cite this