Introducing the theory of probabilistic hierarchical learning for classification

Ziauddin Ursani*, Jo Dicks

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

This is the 5th paper in our series of papers on hierarchical learning for classification. Hierarchical learning for classification is an automated method of creating hierarchy list of learnt models that are on the one hand capable of partitioning the training set into equal number of subsets and on the other hand are also capable of classifying elements of each corresponding subset into classes of the problem. In this paper, the probabilistic hierarchical learning for classification has been formalized and presented as a theory. The theory asserts that the accurate models of complex datasets can be produced through hierarchical application of low complexity models. The theory is validated through experiments on five popular real-world datasets. Generalizing ability of the theory is also tested. Comparison with the contemporary literature points towards promising future for this theory. The theory is covered by four postulates, which are carved out elegantly through mathematical formalisms.

Original languageEnglish
Title of host publicationAdvances and Trends in Artificial Intelligence. From Theory to Practice - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Proceedings
EditorsFranz Wotawa, Ingo Pill, Roxane Koitz-Hristov, Gerhard Friedrich, Moonis Ali
PublisherSpringer Verlag
Pages628-641
Number of pages14
ISBN (Print)9783030229986
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019 - Graz, Austria
Duration: 9 Jul 201911 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11606 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019
Country/TerritoryAustria
CityGraz
Period9/07/1911/07/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

  • Hierarchical learning
  • Probabilistic learning
  • Set-partitioning

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