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Optimal multi-splitting of numeric ranges for decision tree induction
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| dc.contributor.author | Lutu, PEN
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| dc.contributor.editor | Renaud, K.
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| dc.contributor.editor | Kotze, P
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| dc.contributor.editor | Barnard, A
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| dc.date.accessioned | 2018-08-23T10:14:54Z | |
| dc.date.available | 2018-08-23T10:14:54Z | |
| dc.date.issued | 2001 | |
| dc.identifier.citation | Lutu. P.E.N. (2001) Optimal multi-splitting of numeric ranges for decision tree induction. Hardware, Software and Peopleware: Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists, University of South Africa, Pretoria, 25-28 September 2001 | en |
| dc.identifier.isbn | 1-86888-195-4 | |
| dc.identifier.uri | http://hdl.handle.net/10500/24762 | |
| dc.description.abstract | Data mining is the process of extracting informative patterns from data stored in a database or data warehouse. Decision tree induction algorithms, from the area of machine learning are well suited for building classification models in data mining. The handling of continuous-valued attributes in decision tree induction has received a lot of research attention in recent years. Typically, an evaluation function is used to dynamically select the best multi-split for the range of values of a continuous-valued attribute. This paper discusses useful and well behaved evaluation functions and proposes an algorithm for optimal multi-splitting. | en |
| dc.language.iso | en | en |
| dc.subject | Knowledge discovery in databases | en |
| dc.subject | Machine learning | en |
| dc.subject | Data mining | en |
| dc.subject | Decision tree induction | en |
| dc.subject | Classification | en |
| dc.title | Optimal multi-splitting of numeric ranges for decision tree induction | en |
