Post classification label refinement using implicit ordering constraint among data instances

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

6 Scopus citations

Abstract

Classification of instances into different categories in various real world applications suffer from inaccuracies due to lack of representative training data, limitations of classification models, noise and outliers in the input data etc. In this paper we propose a new post classification label refinement method for the scenarios where data instances have an inherent ordering among them that can be leveraged to correct inconsistencies in class labels. We show that by using the ordering constraint, more robust algorithms can be developed than traditional methods. Moreover in most applications where this ordering among instances exists, it is not directly observed. The proposed approach simultaneously estimates the latent ordering among instances and corrects the class labels. We demonstrate the utility of the approach for the application of monitoring the dynamics of lakes and reservoirs. The proposed approach has been evaluated on synthetic datasets with different noise structures and noise levels.

Original languageEnglish (US)
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsCharu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages799-804
Number of pages6
ISBN (Electronic)9781467395038
DOIs
StatePublished - Jan 5 2016
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2016-January
ISSN (Print)1550-4786

Other

Other15th IEEE International Conference on Data Mining, ICDM 2015
CountryUnited States
CityAtlantic City
Period11/14/1511/17/15

Keywords

  • Post classification label refinement
  • Preference based ordering
  • Rank aggregation

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    Khandelwal, A., Mithal, V., & Kumar, V. (2016). Post classification label refinement using implicit ordering constraint among data instances. In C. Aggarwal, Z-H. Zhou, A. Tuzhilin, H. Xiong, & X. Wu (Eds.), Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015 (pp. 799-804). [7373392] (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2016-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2015.149