Abstract
Function decomposition is a promising mechanism for machine learning. This paper investigates its use as a redundancy removal and feature construction preprocessor. Experiments show that its combination with naive Bayesian classifier and decision trees is especially successful on artificial domains while results on real-world data are less encouraging.
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This work is a part of the first author’s PhD thesis.
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Demšar, J., Zupan, B., Bratko, I. (2001). Transformation of attribute space by function decomposition. In: Della Riccia, G., Lenz, HJ., Kruse, R. (eds) Data Fusion and Perception. International Centre for Mechanical Sciences, vol 431. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2580-9_12
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DOI: https://doi.org/10.1007/978-3-7091-2580-9_12
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