Abstract
Classification is one of the most important areas of machine learning. However, there are numerous applications where the quantity of attributes is very large, rendering the usage of conventional classifiers very slow or even impossible. The classifier method in this paper is proposed for such problems. Using the assumption that very large problem spaces are typically sparse as well (considering the stored knowledge), it maps the multi-dimensional problem space into a sequential combination of two-dimensional subdomains. The classifier is easy to implement, fast, and capable of recognizing patterns that are similar to known ones.
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Acknowledgments
This work has partially been sponsored by the Hungarian National Scientific Fund under contract OTKA 105846 and the Research and 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.
The breast cancer databases has been obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.
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Várkonyi-Kóczy, A.R., Tusor, B., Tóth, J.T. (2017). A Multi-attribute Classification Method to Solve the Problem of Dimensionality. In: Jabłoński, R., Szewczyk, R. (eds) Recent Global Research and Education: Technological Challenges. Advances in Intelligent Systems and Computing, vol 519. Springer, Cham. https://doi.org/10.1007/978-3-319-46490-9_54
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DOI: https://doi.org/10.1007/978-3-319-46490-9_54
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