Automatic detection and correction of multi-class classification errors using system whole-part relationships

Zhengzhang Chen, John Jenkins, Alok Nidhi Choudhary, Jinfeng Rao, Fredrick Semazzi, Anatoli V. Melechko, Vipin Kumar, Nagiza F. Samatova*

*Corresponding author for this work

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

1 Scopus citations

Abstract

Real-world dynamic systems such as physical and atmosphere-ocean systems often exhibit a hierarchical system-subsystem structure. However, the paradigm of making this hierarchical/modular structure and the rich properties they encode a "first-class citizen" of machine learning algorithms is largely absent from the literature. Furthermore, traditional data mining approaches focus on designing new classifiers or ensembles of classifiers, while there is a lack of study on detecting and correcting prediction errors of existing forecasting (or classification) algorithms. In this paper, we propose Detector, a hierarchical method for detecting and correcting forecast errors by employing the whole-part relationships between the target system and non-target systems. Experimental results show that Detector can successfully detect and correc-t forecasting errors made by state-of-art classifier ensemble techniques and traditional single classifier methods at an average rate of 22%, corresponding to a 11% average forecasting accuracy increase, in seasonal forecasting of hurricanes and landfalling hurricanes in North Atlantic and North African rainfall.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2013, SMD 2013
PublisherSociety for Industrial and Applied Mathematics Publications
Pages494-502
Number of pages9
ISBN (Electronic)9781627487245
DOIs
StatePublished - Jan 1 2013
Event13th SIAM International Conference on Data Mining, SMD 2013 - Austin, United States
Duration: May 2 2013May 4 2013

Publication series

NameSIAM International Conference on Data Mining 2013, SMD 2013

Other

Other13th SIAM International Conference on Data Mining, SMD 2013
CountryUnited States
CityAustin
Period5/2/135/4/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Signal Processing
  • Software

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    Chen, Z., Jenkins, J., Choudhary, A. N., Rao, J., Semazzi, F., Melechko, A. V., Kumar, V., & Samatova, N. F. (2013). Automatic detection and correction of multi-class classification errors using system whole-part relationships. In SIAM International Conference on Data Mining 2013, SMD 2013 (pp. 494-502). (SIAM International Conference on Data Mining 2013, SMD 2013). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611972832.55