Abstract
A classifier aims to categorize instances into well-defined groups based on a model called classifier. One of the most widely used classifiers is a decision tree built using a recursive partitioning algorithm. This paper applies the recursive partitioning technique based on the series of tubes. A tube is identified from three information; 1) a core vector, 2) a tube length and 3) a tube radius. The first component is the core vector generated by the extreme pole and the centroid of the current dataset and the second component is the tube length which is the maximum magnitude of the projections from all instances onto the core vector and the last component is the tube radius which is the maximum distance of the farthest point away from the core vector. Our experiment was performed on synthesized datasets of varying sizes with 2, 4, 6 and 8 attributes. The results showed the improvement over the conditional inference tree and C4.5 tree via the F-measure and G-measure score.
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Kanchanasuk, S., Sinapiromsaran, K. (2016). Recursive Binary Tube Partitioning for Classification. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_8
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DOI: https://doi.org/10.1007/978-3-319-27000-5_8
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