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.
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Acknowledgements
This work has partially been sponsored by the Hungarian National Scientific Fund under contract OTKA 105846 and the Research & Development Operational Program for the project “Modernization and Improvement of Technical Infrastructure for Research and Development of J. Selye University in the Fields of Nanotechnology and Intelligent Space”, ITMS 26210120042, co-funded by the European Regional Development Fund.
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Tusor, B., Várkonyi-Kóczy, A.R., Tóth, J.T. (2018). A Fuzzy Data Structure for Variable Length Data and Missing Value Classification. In: Luca, D., Sirghi, L., Costin, C. (eds) Recent Advances in Technology Research and Education. INTER-ACADEMIA 2017. Advances in Intelligent Systems and Computing, vol 660. Springer, Cham. https://doi.org/10.1007/978-3-319-67459-9_37
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DOI: https://doi.org/10.1007/978-3-319-67459-9_37
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