Multi-Class Classification with Asymmetric Error Control for Medical Disease Diagnosis

Wasif Bokhari, James Smith, Ajay Bansal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There is a growing need for asymmetric error control in medical disease diagnosis because the cost of a false negative may greatly exceed the cost of a false positive. This paper aims to extend the asymmetric error control achieved using the Neyman-Pearson (NP) Lemma in binary classification to multiclass classification. The NP oracle inequalities for binary classes are not immediately applicable for the multiclass NP classification, leading to a multi-step procedure to extend the algorithm in the context of multiple classes. Firstly, a hierarchical order of severity for misclassification for each class is maintained and the most critical classes identified. Secondly, the NP methods are applied to classify the most critical class versus the rest, and then the NP methods are applied to the next most severe class in the list versus the rest until a label is assigned. This approach is used to construct a novel tree-based classifier that enables asymmetric error control for multiclass classification and is evaluated on a cardiac arrhythmia dataset, where missing certain types of arrhythmia have more severe consequences than missing others. The results show that we are able to control the number of false negatives for the most critical classes in the multiclass classification problem.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 4th International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages88-94
Number of pages7
ISBN (Electronic)9781665437363
DOIs
StatePublished - 2021
Externally publishedYes
Event4th IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2021 - Laguna Hills, United States
Duration: Dec 1 2021Dec 3 2021

Publication series

NameProceedings - 2021 IEEE 4th International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2021

Conference

Conference4th IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2021
Country/TerritoryUnited States
CityLaguna Hills
Period12/1/2112/3/21

Keywords

  • error control
  • machine learning
  • multiclass classification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management

Fingerprint

Dive into the research topics of 'Multi-Class Classification with Asymmetric Error Control for Medical Disease Diagnosis'. Together they form a unique fingerprint.

Cite this