ASF/DT, adaptive step forward decision tree construction
Taizhe Tan, Ying-Yi Liang · 2012
This paper presents a novel and efficient decision tree construction approach based on C4.5. C4.S constructs decision tree with information gain ratio and deals with missing values or noise. ID3 and its improvement, C4.5, both select one attribute as the splitting criterion each time during constructing decision tree, adopting one step forward. Comparing with one step forward, the proposed algorithm, ASF/DT in the paper would use either one attribute or two attributes as the splitting criterion for establishing tree nodes, adopting adaptive step forward that would improve the possibility in finding the optima. Given 3 UCI standard datasets, the experimental results prove its performance and efficiency in constructing decision tree.