Decision Machines: Interpreting Decision Tree as a Model Combination Method
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Based on decision trees, it is efficient to handle tabular data. Conventional decision tree growth methods often result in suboptimal trees because of their greedy nature. Their inherent structure limits the options of hardware to implement decision trees in parallel. Here is a compact representation of binary decision trees to overcome these deficiencies. We explicitly formulate the dependence of prediction on binary tests for binary decision trees and construct a function to guide the input sample from the root to the appropriate leaf node. And based on this formulation we introduce a new interpretation of binary decision trees. Then we approximate this formulation via continuous functions. Finally, we interpret the decision tree as a model combination method. And we propose the selection-prediction scheme to unify a few learning methods.
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