Generation of regression trees using reinforcement learning
نویسندگان
چکیده
We present a novel methodology for regression trees generation that uses the reinforcement learning frame for learning efficient regression trees. We describe the basic variant of such a methodology that uses the Monte-Carlo method to explore the space of possible regression trees. Comparison with other methods of regression is performed and evaluated. Our algorithm is implemented as a software program in the JAVA programming language and uses the framework of WEKA machine learning library. This work can be seen as a step toward on-line learning methodology for generation of decision and regression trees on drifting concepts.
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