Abstract Estimating forest above‐ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30‐ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand density variable as input across a series of area scales using the Random Forest (RF) algorithm. As the grain size increased from 10 to 200 m, we found that the relative importance of explanatory variables that drove the variation of biomass productivity varied a lot, and the model accuracy was gradually improved. The minimum sampling area for biomass productivity modeling in this region was 140 × 140 m. Our study shows that the relationship of topography, species diversity, stand structure, and stand density variables with biomass productivity modeled using the RF algorithm changes when moving from scales typical of forest surveys (10 m) to larger scales (200 m) within a controlled methodology. These results should be of considerable interest to scientists concerned with forest assessment.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.9110

Assessing scale‐dependent effects on Forest biomass productivity based on machine learning

INTRODUCTION Curriculum learning through the wisdom tree massive open online course platform not only gets rid of the limitations of specialty, school and region, eliminates the limitations of time and space in traditional teaching, but also effectively solves the problem of educational equity. OBJECTIVES This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. METHODS This study takes the university students in Fuzhou city information management department as the survey object, and adopts the electronic questionnaire survey method. A total of 1136 formal questionnaires were responded, and 1028 valid questionnaires were obtained after data cleaning and deleting invalid questionnaires (the effective rate was 90.49%). In this paper, the reliability and validity of the questionnaire were tested by IBM SPSS-20.0 software, and six explanatory variables including function, achievement, exercise, quality, richness, and interaction were obtained by principal component analysis. Then, the questionnaire data is converted to CSV (comma separated values) format for analysis. This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. In this paper, the proposed algorithm is compared with decision tree, random forest, k-nearest neighbor, and support vector machine to verify its performance. RESULTS The experimental results show that training set classification accuracy of decision tree, random forest, k-nearest neighbor, only support vector machine and the proposed algorithm (simulated annealing + support vector machine) are 92.21%, 96.10%, 95.67%, 97.29%, and 99.58%, respectively. CONCLUSION The proposed algorithm simulated annealing + support vector machine does increase the classification accuracy. At the same time, the 11 decision rules generated by simulated annealing + decision tree can provide useful information for decision makers.

Study on the university students' satisfaction of the wisdom tree massive open online course platform based on parameter optimization intelligent algorithm.

With the continuous progress and development of Internet and information technology, online milk tea has gradually entered the public eye. The consumers' consumption psychology in online milk tea consumption decision-making is widely valued by practitioners in milk tea industry. How to effectively analyze consumers' psychological decision-making process has become the main topic at present. Taking Fuzhou City as an example, this study uses IBM SPSS20.0 software to test the reliability and validity of the questionnaire. Seven explanatory variables and one target variable were determined by principal component analysis (PCA). Four algorithms, decision tree (DT), random forest (RF), support vector machine (SVM) and k-nearest neighbor (KNN), were used to establish the relationship between target variables and explanatory variables, and the consumer psychology of online milk tea was analyzed. Intelligent algorithm is used to combine simulated annealing (SA) algorithm with SVM to realize automatic parameter adjustment and improve classification accuracy. The experimental results show that the decision tree obtains six decision rules that affect consumers' psychology of buying online milk tea, which can be used as an important decision to judge whether consumers agree to buy online milk tea, and help the milk tea industry to establish a better foothold and provide valuable reference.

Parameter Optimized Intelligent Algorithm for Consumer Psychology of Web Red Milk Tea Purchases

Nowadays, in the topics related to prediction, in addition to increasing the accuracy of existing algorithms, the reduction of computational time is a challenging issue that has attracted much attention. Since the existing methods may not have enough efficiency and accuracy, we use a combination of machine-learning algorithms and statistical methods to solve this problem. Furthermore, we reduce the computational time in the testing model by automatically reducing the number of trees using penalized methods and ensembling the remaining trees. We call this efficient combinatorial method “ensemble of clustered and penalized random forest (ECAPRAF)”. This method consists of four fundamental parts. In the first part, k-means clustering is used to identify homogeneous subsets of data and assign them to similar groups. In the second part, a tree-based algorithm is used within each cluster as a predictor model; in this work, random forest is selected. In the next part, penalized methods are used to reduce the number of random-forest trees and remove high-variance trees from the proposed model. This increases model accuracy and decreases the computational time in the test phase. In the last part, the remaining trees within each cluster are combined. The results of the simulation and two real datasets based on the WRMSE criterion show that our proposed method has better performance than the traditional random forest by reducing approximately 12.75%, 11.82%, 12.93%, and 11.68% and selecting 99, 106, 113, and 118 trees for the ECAPRAF–EN algorithm.

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An Ensemble Framework to Improve the Accuracy of Prediction Using Clustered Random-Forest and Shrinkage Methods

This article is improved the random forest algorithm by selecting the most appropriate penalized regression methods, and it is tried to improve the post-selection boosting random forest (PBRF) algorithm using elastic net regression. The proposed method with the highest efficiency is called Reducing and Aggregating Random Forest Trees by Elastic Net (RARTEN). The introduced method consists of three steps. In the first step, the random forest algorithm is used as a predictor. In the second step, Elastic Net, as a penalized regression method, is applied to reduce the number of trees and improve the random forest and PBRF. In the last step, selected trees are aggregated. The obtained results of the real data and Monte Carlo simulation are evaluated using various statistical performance criteria. The simulation study shows that the RARTEN with 7%, 5%, and 8.5% reduction in the linear, nonlinear, and noise model, respectively improve the accuracy of the traditional random forest and the proposed method by Wang. In addition, this method has a significant reduction compared to other penalized regression methods. Moreover, the real data results show that the proposed method in our study with a reduction of almost 16% confirms the validity of the proposed model. 

Improving random forest algorithm by selecting appropriate penalized method

The random forest (RF) algorithm is a very practical and excellent ensemble learning algorithm. In this paper, we improve the random forest algorithm and propose an algorithm called ‘post-selection...

Hui Wang

Papers

Improving random forest algorithm by Lasso method