Automatic Selection of Split Criterion during Tree Growing Based on Node Location
作者： Carla E. Brodley
DOI: 10.1016/B978-1-55860-377-6.50018-9
关键词: Fractal tree index 、 Algorithm 、 Search tree 、 Artificial intelligence 、 Tree (data structure) 、 Interval tree 、 Vantage-point tree 、 Pattern recognition 、 Tree traversal 、 Mathematics 、 Incremental decision tree 、 Segment tree
摘要: Abstract Typically, decision tree construction algorithms apply a single “goodness of split” criterion to form each test node the tree. It is hypothesis this research that better results can be obtained if during one applies split suited “location” in Specifically, given objective maximizing predictive accuracy, nodes near root should chosen using measure based on information theory, whereas closer leaves pruned maximize classification accuracy training set. The an empirical evaluation illustrate adapting location improve performance.
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