NDLI: Reinforced random forest

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Reinforced random forest

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Reinforced random forest

Content Provider	ACM Digital Library
Author	Mukherjee, Dipti Prasad Paul, Angshuman
Abstract	Reinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier. We propose a reinforced random forest (RRF) classifier that exploits reinforcement learning to improve classification accuracy. Our algorithm is initialized with a forest. Then the entire training data is tested using the initial forest. In order to reinforce learning, we use mis-classified data points to grow certain number of new trees. A subset of the new trees is added to the existing forest using a novel graph-based approach. We show that addition of these trees ensures improvement in classification accuracy. This process is continued iteratively until classification accuracy saturates. The proposed RRF has low computational burden. We achieve at least 3% improvement in F-measure compared to random forest in three breast cancer datasets. Results on benchmark datasets show significant reduction in average classification error.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450347532
DOI	10.1145/3009977.3010003
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Classification accuracy Reinforcement learning Shortest path Random forest
Content Type	Text
Resource Type	Article

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