Research on Decision Forest Learning Algorithm | Zendy

Limin Wang | Zendy; Xiongfei Li | Zendy

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Research on Decision Forest Learning Algorithm

Author(s) -

Limin Wang,

Xiongfei Li

Publication year - 2008

Publication title -

computer and information science

Language(s) - English

Resource type - Journals

eISSN - 1913-8997

pISSN - 1913-8989

DOI - 10.5539/cis.v1n2p17

Subject(s) - overfitting , computer science , decision tree , machine learning , artificial intelligence , tree (set theory) , root (linguistics) , tree structure , incremental decision tree , decision tree learning , algorithm , alternating decision tree , data mining , mathematics , artificial neural network , binary tree , mathematical analysis , linguistics , philosophy

Decision Forests are investigated for their ability to provide insight into the confidence associated with each prediction, the ensembles increase predictive accuracy over the individual decision tree model established. This paper proposed a novel “bottom-top” (BT) searching strategy to learn tree structure by combining different branches with the same root, and new branches can be created to overcome overfitting phenomenon.

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