A Searching Method of Candidate Segmentation Point in SPRINT Classification
2016 ◽
Vol 2016
◽
pp. 1-5
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Keyword(s):
SPRINT algorithm is a classical algorithm for building a decision tree that is a widely used method of data classification. However, the SPRINT algorithm has high computational cost in the calculation of attribute segmentation. In this paper, an improved SPRINT algorithm is proposed, which searches better candidate segmentation point for the discrete and continuous attributes. The experiment results demonstrate that the proposed algorithm can reduce the computation cost and improve the efficiency of the algorithm by improving the segmentation of continuous attributes and discrete attributes.
2012 ◽
Vol 2
(1)
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pp. 7-9
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2021 ◽
pp. 1045389X2110263
2019 ◽
Vol 2019
(1)
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2006 ◽
Vol 04
(03)
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pp. 639-647
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2021 ◽
Vol 12
(4)
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pp. 118-131
2021 ◽