A fuzzy data structure for variable length data and missing value classification

Balazs Tusor, A. Várkonyi-Kóczy, János T. Tóth

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

1 Citation (Scopus)

Abstract

Variable length data classification is an important field of machine learning. However, while there are plenty of classifiers in literature that can efficiently handle fixed length data, not many can also handle data with varying length samples. In this paper, a structure is proposed for quick and robust classification of such data, as well as data sets with occasionally missing values. It builds on the principle of look-up table classifiers, realizing a direct assignment between the attribute values of the given data samples and their corresponding classes. The proposed data structure solves this problem by decomposing the problem space into a sequence of integer value combinations, thus creating and maintaining a layered structure in the combined form of 1D and 2D arrays. Furthermore, a simple analysis regarding the data structure can reveal functional dependencies considering the attributes of the data set, offering an option to simplify the structure thus reduce its complexity.

Original languageEnglish
Title of host publicationRecent Advances in Technology Research and Education - Proceedings of the 16th International Conference on Global Research and Education Inter-Academia 2017
PublisherSpringer Verlag
Pages297-304
Number of pages8
ISBN (Print)9783319674582
DOIs
Publication statusPublished - Jan 1 2018
Event16th International Conference on Global Research and Education Inter-Academia, 2017 - Iasi, Romania
Duration: Sep 25 2017Sep 28 2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume660
ISSN (Print)2194-5357

Other

Other16th International Conference on Global Research and Education Inter-Academia, 2017
CountryRomania
CityIasi
Period9/25/179/28/17

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Keywords

  • Classification
  • Data mining
  • Data structure
  • Machine learning
  • Missing data
  • Pattern recognition

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Tusor, B., Várkonyi-Kóczy, A., & Tóth, J. T. (2018). A fuzzy data structure for variable length data and missing value classification. In Recent Advances in Technology Research and Education - Proceedings of the 16th International Conference on Global Research and Education Inter-Academia 2017 (pp. 297-304). (Advances in Intelligent Systems and Computing; Vol. 660). Springer Verlag. https://doi.org/10.1007/978-3-319-67459-9_37