Proceedings ArticleDOI
Reinforced random forest
Angshuman Paul,Dipti Prasad Mukherjee +1 more
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TLDR
This work proposes a reinforced random forest (RRF) classifier that exploits reinforcement learning to improve classification accuracy and achieves at least 3% improvement in F-measure compared to random forest in three breast cancer datasets.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.read more
Citations
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Toward a Smart Cloud: A Review of Fault-Tolerance Methods in Cloud Systems
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Online human activity recognition using low-power wearable devices
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Online Human Activity Recognition using Low-Power Wearable Devices
TL;DR: This paper presents the first HAR framework that can perform both online training and inference, and starts with a novel technique that generates features using the fast Fourier and discrete wavelet transforms of a textile-based stretch sensor and accelerometer data.
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M-ary Random Forest - A new multidimensional partitioning approach to Random Forest
Vikas Jain,Ashish Phophalia +1 more
TL;DR: This work empirically proves that the performance of the MaRF improves due to the improvement in the strength of the M-ary trees, and proposes to use multiple features at a node for splitting the data as in axis parallel method.
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Quantifying Plant Species α-Diversity Using Normalized Difference Vegetation Index and Climate Data in Alpine Grasslands
TL;DR: In this article , Zhang et al. used six methods (i.e., random forest, generalized boosted regression, artificial neural network, multiple linear regression, support vector machine and recursive regression trees) for quantifying plant α-diversity of grasslands at multiple spatial and temporal scales.
References
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Journal ArticleDOI
Locating Facial Landmarks Using Probabilistic Random Forest
TL;DR: The proposed PRF is the same as standard random forest except that it models the probability of a sample belonging to the nodes of a tree, and significantly outperforms the state-of-the-art in terms of accuracy.
Proceedings ArticleDOI
Enhanced Random Forest for Mitosis Detection
TL;DR: This work proposes a fast and accurate approach for automatic mitosis detection from histopathological images using an enhanced random forest classifier with weighted random trees.