Two-layer random forests model for case reuse in case-based reasoning

Zhong, Shisheng; Xie, Xu; Lin, Lin

doi:10.1016/j.eswa.2015.08.005

Cited by 24 publications

(1 citation statement)

References 40 publications

(26 reference statements)

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“…Specifically, at each internal node, the algorithm searches the values of the incoming dataset and recognizes a threshold for one predictor variable to split the dataset such that the homogeneity of dependent variable values in each branch is maximized. In the RFR, each decision tree is trained using a subset of data randomly sampled with replacement from the original training dataset, which can increase the robustness against overfitting [50]. In order to inject an additional layer of randomness, instead of using all variables, only a subset of randomly selected variables are considered to form the split nodes of each tree [51].…”

Section: Random Forest Regressionmentioning

confidence: 99%

Water Price Prediction for Increasing Market Efficiency Using Random Forest Regression: A Case Study in the Western United States

Lian

Bin

et al. 2019

Water

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The existence of water markets establishes water prices, promoting trading of water from low- to high-valued uses. However, market participants can face uncertainty when asking and offering prices because water rights are heterogeneous, resulting in inefficiency of the market. This paper proposes three random forest regression models (RFR) to predict water price in the western United States: a full variable set model and two reduced ones with optimal numbers of variables using a backward variable elimination (BVE) approach. Transactions of 12 semiarid states, from 1987 to 2009, and a dataset containing various predictors, were assembled. Multiple replications of k-fold cross-validation were applied to assess the model performance and their generalizability was tested on unused data. The importance of price influencing factors was then analyzed based on two plausible variable importance rankings. Results show that the RFR models have good predictive power for water price. They outperform a baseline model without leading to overfitting. Also, the higher degree of accuracy of the reduced models is insignificant, reflecting the robustness of RFR to including lower informative variables. This study suggests that, due to its ability to automatically learn from and make predictions on data, RFR-based models can aid water market participants in making more efficient decisions.

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Section: Random Forest Regressionmentioning

confidence: 99%