A novel multiple classifier generation and combination framework based on fuzzy clustering and individualized ensemble construction

Zhen Gao, Maryam Zand, Jianhua Ruan

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

Abstract

Multiple classifier system (MCS) has become a successful alternative for improving classification performance. However, studies have shown inconsistent results for different MCSs, and it is often difficult to predict which MCS algorithm works the best on a particular problem. We believe that the two crucial steps of MCS - base classifier generation and multiple classifier combination, need to be designed coordinately to produce robust results. In this work, we show that for different testing instances, better classifiers may be trained from different subdomains of training instances including, for example, neighboring instances of the testing instance, or even instances far away from the testing instance. To utilize this intuition, we propose Individualized Classifier Ensemble (ICE). ICE groups training data into overlapping clusters, builds a classifier for each cluster, and then associates each training instance to the top-performing models while taking into account model types and frequency. In testing, ICE finds the k most similar training instances for a testing instance, then predicts class label of the testing instance by averaging the prediction from models associated with these training instances. Evaluation results on 49 benchmarks show that ICE has a stable improvement on a significant proportion of datasets over existing MCS methods. ICE provides a novel choice of utilizing internal patterns among instances to improve classification, and can be easily combined with various classification models and applied to many application domains.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
EditorsLisa Singh, Richard De Veaux, George Karypis, Francesco Bonchi, Jennifer Hill
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages231-240
Number of pages10
ISBN (Electronic)9781728144931
DOIs
StatePublished - Oct 2019
Externally publishedYes
Event6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019 - Washington, United States
Duration: Oct 5 2019Oct 8 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019

Conference

Conference6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
CountryUnited States
CityWashington
Period10/5/1910/8/19

Keywords

  • Classification
  • Ensemble Learning
  • Instance Selection
  • Model Selection
  • Multiple classifier system

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Gao, Z., Zand, M., & Ruan, J. (2019). A novel multiple classifier generation and combination framework based on fuzzy clustering and individualized ensemble construction. In L. Singh, R. De Veaux, G. Karypis, F. Bonchi, & J. Hill (Eds.), Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019 (pp. 231-240). [8964130] (Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2019.00038