SPAARC: A Fast Decision Tree Algorithm
| Autor: | Darren Yates, Junbin Gao, Zahidul Islam |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Decision tree learning Decision tree Process (computing) Sampling (statistics) Feature selection 02 engineering and technology Information repository computer.software_genre 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Knowledge extraction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Node (circuits) Data mining computer |
| Zdroj: | Communications in Computer and Information Science ISBN: 9789811366604 AusDM |
| DOI: | 10.1007/978-981-13-6661-1_4 |
| Popis: | Decision trees are a popular method of data-mining and knowledge discovery, capable of extracting hidden information from datasets consisting of both nominal and numerical attributes. However, their need to test the suitability of every attribute at every tree node, in addition to testing every possible split-point for every numerical attribute can be expensive computationally, particularly for datasets with high dimensionality. This paper proposes a method for speeding up the decision tree induction process called SPAARC, consisting of two components to address these issues – sampling of the numeric attribute tree-node split-points and dynamically adjusting the node attribute selection space. Further, these methods can be applied to almost any decision tree algorithm. To confirm its validity, SPAARC has been tested and compared against an implementation of the CART algorithm using 18 freely-available datasets from the UCI data repository. Results from this testing indicate the two components of SPAARC combined have minimal effect on decision tree classification accuracy yet reduce model build times by as much as 69%. |
| Databáze: | OpenAIRE |
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