Parallel Decision Tree Algorithm Based on Combination
- Resource Type
- Conference
- Authors
- Wenlong, Li; Changzheng, Xing
- Source
- 2010 International Forum on Information Technology and Applications Information Technology and Applications (IFITA), 2010 International Forum on. 1:99-101 Jul, 2010
- Subject
- Computing and Processing
Decision trees
Heuristic algorithms
Program processors
Classification algorithms
Algorithm design and analysis
Data mining
Training
parallel
decision tree
ID3
combination classification
knowledge integration
- Language
Early ID3, C4.5, CART and the other decision tree algorithms are no longer met the situation of massive data analysis for the time being. Those algorithms has the same limitations that they can not handle the updated data sets dynamically and the decision tree generated by these algorithms need to be purned. These weaknesses limit the use of the above-mentioned algorithms. So a novel parallel decision tree classification algorithm based on combination (PCDT) is put forward in this paper. This algorithm has the excellent features that it can be updated when the data set is renewing and it is scalable and no pruning. It has been proved by the experiment that the PCDT algorithm has higher classification accuracy and is easy to parallel.