Temporal exceptional model mining using dynamic Bayesian networks

Marcos L.P. Bueno*, Arjen Hommersom, Peter J.F. Lucas

*Corresponding author for this work

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

Abstract

The discovery of subsets of data that are characterized by models that differ significantly from the entire dataset, is the goal of exceptional model mining. With the increasing availability of temporal data, this task has clear relevance in discovering deviating temporal subprocesses that can bring insight into industrial processes, medical treatments, etc. As temporal data is often noisy, high-dimensional and has complex statistical dependencies, discovering such temporal subprocesses is challenging for current exceptional model mining methods. In this paper, we introduce Temporal Exceptional Model Mining to capture multiple and complex relationships among temporal variables of a dataset in a principled way. Our contributions are as follows: (i) we define the new task of temporal exceptional model mining; (ii) we characterize the discovery of exceptional temporal submodels using dynamic Bayesian networks by means of a new distance measure, (iii) we introduce a search procedure for exceptional dynamic Bayesian networks optimized by properties of the proposed distance, and (iv) the practical value of the proposed method is demonstrated based on simulated data and process data of funding applications and by comparisons with other exceptional model mining methods.

Original languageEnglish
Title of host publicationAdvanced Analytics and Learning on Temporal Data - 5th ECML PKDD Workshop, AALTD 2020, Revised Selected Papers
EditorsVincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim
PublisherSpringer
Pages97-112
Number of pages16
ISBN (Print)9783030657413
DOIs
Publication statusPublished - 2020
Event5th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2020 - Online Event, Ghent, Belgium
Duration: 18 Sep 202018 Sep 2020
Conference number: 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12588 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

Workshop5th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2020
Abbreviated titleAALTD 2020
CountryBelgium
CityGhent
Period18/09/2018/09/20

Keywords

  • Bayesian networks
  • Exceptional model mining
  • Graphical models
  • Machine learning
  • Subgroup discovery
  • Temporal data

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