Random Forest Algorithm Model Research Articles

Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

In order to further improve the identification efficiency of tobacco mildew, a rapid identification model of tobacco mildew based on random forest algorithm was proposed in this study. In order to ensure the feasibility and pertinence of the model study, this study takes redried leaf tobacco as the research object, selects high-temperature and high-humidity environment as the experimental conditions, and obtains the sample data of the degree of tobacco mildew under different experimental conditions. At the same time, this paper constructs a rapid identification model of tobacco mildew with the help of random forest algorithm. Through the model experimental results, it is found that the accuracy of the model for the rapid identification of training samples can reach 93.82%, while the accuracy of independent testing is 94.84%. The experimental results fully reflect the availability and efficiency of the random forest algorithm model in the rapid identification of tobacco mildew.

Application of Mathematical Model Using Random Forest in Performance Appraisal Management of Cadres in Free Trade Zone.

To improve the efficiency of scientific assessment of cadre performance, first, this work analyzes the current situation of cadre performance appraisal in the free trade zone under the background of big data, and introduces the free trade zone and Random Forest (RF) algorithm. Second, based on the cadre evaluation index, this work establishes the cadre performance evaluation system of the free trade zone. Finally, the random forest algorithm model is implemented for the performance evaluation of cadres in the free trade zone. Additionally, the model's performance is verified with the actual data, including the acquisition of the best parameters and the most important indicators of the model and the performance comparison between the RF algorithm and other models. The results show that the performance of cadres in the free trade zone is finally divided into four grades: medium, good, qualified, and excellent. There are obvious grade differences in the performance of cadres in the free trade zone. Partly because some qualified cadres lack a strong sense of competition and professional competence, do not publicize the work of cadres in the free trade zone, and do not communicate with the masses in time. In the data processing, 18 missing experimental data were supplemented, and the best model parameters were obtained as follows: NTree = 200, MTry = 1. The most important indicators of cadre performance evaluation are the construction of a clean and honest government, the ability to act in accordance with the law and the professional ability. The accuracy of the RF algorithm obtained here is 71.4%. The prediction accuracy of the RF algorithm for the overall sample, training sample, and test sample is 94%, 96%, and 86%, respectively, which are higher than those of other common models. A RF algorithm with good classification effect is obtained and this work provides a reference for the scientific management of cadre performance appraisal.

Construction and validation of gastric cancer diagnosis model based on machine learning

Aim: To screen differentially expressed genes related to gastric cancer based on The Cancer Genome Atlas (TCGA) database and construct a gastric cancer diagnosis model by machine learning. Methods: Transcriptional data, genomic data, and clinical information of gastric cancer tissues and non-gastric cancer tissues were downloaded from the TCGA database, and differentially expressed genes of gastric cancer messenger RNA (mRNA) and long non-coding RNA (lncRNA) were screened out. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzed the differentially expressed genes, and the protein-protein interaction (PPI) of differentially expressed genes was constructed. Core differentially expressed genes were screened by Cytoscape software’s molecular complex detection (MCODE) plug-in. The differential genes of lncRNA were analyzed by univariate Cox regression analysis and lasso regression for further dimension reduction to obtain the core genes. The core genes were screened by machine learning to construct the gastric cancer diagnosis model. The efficiency of the gastric cancer diagnosis model was verified externally by the Gene Expression Omnibus (GEO) database. Results: Finally, 10 genes including long intergenic non-protein coding RNA 1821 (LINC01821), AL138826.1, AC022164.1, adhesion G protein-coupled receptor D1-antisense RNA 1 (ADGRD1-AS1), cyclin B1 (CCNB1), kinesin family member 11 (KIF11), Aurora kinase B (AURKB), cyclin dependent kinase 1 (CDK1), nucleolar and spindle associated protein 1 (NUSAP1), and TTK protein kinase (TTK) were screened as gastric cancer diagnostic model genes. After efficiency analysis, it was found that the random forest algorithm model had the best comprehensive evaluation, with an accuracy of 92% and an area under the curve (AUC) of 0.9722, which was more suitable for building a gastric cancer diagnosis model. The GSE54129 data set was used to verify the gastric cancer diagnosis model with an AUC of 0.904, indicating that the gastric cancer diagnosis model had high accuracy. Conclusions: Machine learning can simplify the bioinformatics analysis process and improve efficiency. The core gene discovered in this study is expected to become a gene chip for the diagnosis of gastric cancer.

Analysis of Factors of Productivity of Tight Conglomerate Reservoirs Based on Random Forest Algorithm

The tight conglomerate reservoir of Baikouquan formation in the MA 131 well block in the Junggar basin abounds with petroleum reserves, yet the vertical wells in this reservoir have achieved a limited development effect. The tight conglomerate reservoirs have become an important target for exploration and exploitation. The high-efficiency development scheme of a small well spacing three-dimensional (3D) staggered well pattern has been determined by a series of field tests on well pattern and well spacing development. Multistage fracturing with a horizontal well has been demonstrated as the primary development technology. The horizontal wells in the MA 131 small well spacing demonstration area have achieved significantly different development effects, and the major controlling factors for high and stable production of a single well remain unclear. In this study, we proposed an evaluation model of major productivity controlling factors of the tight conglomerate reservoir to provide a reference for oil recovery based on a random forest (RF) machine-learning algorithm. The productivity factors were investigated from two aspects: petrophysical facies that are capable of indicating the genetic mechanism of geological dessert and engineering dessert parameters forming complex fracture networks. Resultantly, the reservoir in the MA 131 well block can be classified into 12 petrophysical facies according to the sedimentary characteristics and diagenesis analysis. The mercury injection curves of a variety of petrophysical facies can be classified into four reservoir quality types. The RF model was trained on 80% of the data to predict the oil well class using the selected features as primary inputs while the remaining 20% of the data were set to test the model performance. The results indicated that the RF model produced excellent results with only 12 misclassifications across the entire data set of 627 samples that represent <2% error. The important evaluation score of the random forest algorithm model showed that the reservoir type, oil saturation, horizontal stress difference, and gravel content are the most important four indicators, with each value exceeding 15%. Brittleness and maximum horizontal stress are considered the least important indexes, with values of less than 5%. Reservoir quality and oil saturation were confirmed as the major controlling factors and material foundation for oil wells’ high and stable production. As indicated in this study, stress difference and gravel content are the major controlling factors in the formation of a complex fracture network.

System Optimization of Talent Life Cycle Management Platform Based on Decision Tree Model

Decision tree algorithm is a widely used classification and prediction method. Because it generates a tree‐like classifier, it has a simple structure and is extensively used by people. Regardless of the decision tree algorithm, the decision attributes are classified according to the condition attributes. The judgment process is from the root node to the leaf node. Each branch of the tree takes the form of selecting the best split attribute. However, this classification method of decision tree makes it rely too much on training data. If the data are more complicated, there are noisy data, incomplete data, etc. The decision tree will often have overfitting problems. This study mainly analyzes the random forest algorithm model and the CART algorithm and applies the CART algorithm to the model according to the random forest model. Aiming at the algorithm’s shortcomings in solving big data, this study will improve the algorithm through the MapReduce programming model to achieve parallelization of the process and construction of the function. Combining the construction goals and principles of the talent supply chain management system, this study constructs the overall framework and operational process of the enterprise talent supply chain management system based on the decision tree model from the overall level and the operational level. Aiming at the enterprise’s talent management problems, it focuses on designing integrated management, flexible management, talent information integrated management, and evaluation and optimization management models to ensure that the constructed system is operable and measurable and can achieve dynamic optimization. Based on the current situation of talent management in a company, this study analyzes the enterprise talent supply chain management model based on the decision tree model proposed in this study and constructs the overall framework and core model of a company’s talent supply chain management system. The current situation of the company puts forward the safeguard measures for the implementation of the management system to assure that the established management system can be effectively implemented.

Prediction of burn‐up nucleus density based on machine learning

Machine learning models were built by using four different algorithms using Linear Regression, Regression Tree, Multi-Layer Perceptron, and Random Forest by 10-fold Cross-Validation method using the training set. The validity of the four different machine learning algorithms was verified by predicting the nuclide densities of 235U, 238U, 239Pu, 241Pu, 137Cs, 244Cm, and 254Nd at different burn-up depths by enrichment and burn-up depth. The experimental results show that the Pearson Correlation Coefficients of the training sets of the four algorithms based on the 10-fold Cross-Validation method are all greater than 0.72, among which the evaluation coefficients of the models of Regression Tree and Random Forest are better than those of the Multi-Layer Perceptron and Linear Regression; however, the prediction based on the test set is found that the model of the Multi-Layer Perceptron predicts better than the other three models, and the average deviation is less than 1% and the average deviation is less than 3% for the Regression Tree and Random Forest algorithm model.

Research and application of data mining algorithm

With the breakthrough development of a series of technologies such as machine learning and internet of things, data plays an increasingly important role in everyone’s daily affairs and work. With the explosive exponential growth of data volume, data types are also quietly changing, from the traditional single, structured data to today’s diverse, semi-structured data. In the face of emerging new massive data, data mining technology has gradually become the focus of attention. Data mining was also known as knowledge discovery (KDD) at that time. It was mainly defined as a pattern hidden in massive data, which must be understood by people and bring potential benefits. In this paper, we mainly studies the basic principle and algorithm knowledge of data mining, and applies ridge regression and random forest algorithm model in real estate price forecasting. Finally, through the stacking thinking of ensemble learning in ensemble learning, we propose a fusion method of ridge regression and random forest model, and obtain a more accurate and stable prediction model.

Seq-SymRF: a random forest model predicts potential miRNA-disease associations based on information of sequences and clinical symptoms

Increasing evidence indicates that miRNAs play a vital role in biological processes and are closely related to various human diseases. Research on miRNA-disease associations is helpful not only for disease prevention, diagnosis and treatment, but also for new drug identification and lead compound discovery. A novel sequence- and symptom-based random forest algorithm model (Seq-SymRF) was developed to identify potential associations between miRNA and disease. Features derived from sequence information and clinical symptoms were utilized to characterize miRNA and disease, respectively. Moreover, the clustering method by calculating the Euclidean distance was adopted to construct reliable negative samples. Based on the fivefold cross-validation, Seq-SymRF achieved the accuracy of 98.00%, specificity of 99.43%, sensitivity of 96.58%, precision of 99.40% and Matthews correlation coefficient of 0.9604, respectively. The areas under the receiver operating characteristic curve and precision recall curve were 0.9967 and 0.9975, respectively. Additionally, case studies were implemented with leukemia, breast neoplasms and hsa-mir-21. Most of the top-25 predicted disease-related miRNAs (19/25 for leukemia; 20/25 for breast neoplasms) and 15 of top-25 predicted miRNA-related diseases were verified by literature and dbDEMC database. It is anticipated that Seq-SymRF could be regarded as a powerful high-throughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/LeeKamlong/Seq-SymRF.

Research on Forecasting of China’s Monetary Policy Based on Random Forest Algorithm

This paper uses the random forest algorithm model to quantify and predict the monetary policy of the People’s Bank of China under the input of 16 macroeconomic indicators. It is compared with three other machine learning algorithms (CART decision tree, support vector machine and neural network algorithm), discrete selection model and combined prediction model. The results show that the random forest algorithm shows better prediction accuracy in predicting the direction of the central bank’s monetary policy.

The exploration of internet finance by using neural network

In order to find out the risks of Internet finance as much as possible, and to ensure the rapid and healthy development of Internet finance, random forest (RF), a common classification algorithm in machine learning, was applied to analyze the risk factors of Internet finance. Additionally, the results of traditional statistical methods were compared with those of RF and back propagation (BP) neural network methods and their performance was evaluated. Finally, some suggestions were given for these risk factors, especially for the problems with high risk. The results showed that the RF algorithm model had the best classification effect and could accurately analyze the risks of Internet finance in terms of market, law, credit, personal information, and professional knowledge. It was found that credit and personal information risk were the most important factors in the future development of Internet finance when BP neural network was used to evaluate these risks. To a certain extent, they would also hinder the use and development of Internet finance. At the same time, it proved that BP neural network had a good prediction effect. To sum up, using the RF algorithm and BP neural network method in machine learning to explore the problems of Internet finance is of great significance for risk prediction for other financial institutions.

Human motion recognition based on Kalman random Forest algorithm and 3D multimedia

A human motion recognition method based on Kalman random forest algorithm model is proposed to improve the accuracy and efficiency of tracking algorithm aiming at the multi-degree-of-freedom problem of traditional human motion recognition and tracking. Firstly, a simple explicit manifold space model of human motion parameters is established. Reconstruction of three-dimensional image features for human motion requires accurate identification of human behavioral features. The human motion process is highly random, and the sudden change in motion amplitude causes a sudden change in the number of shape-based parameters in the three-dimensional feature. The compact low-dimensional model simplifies the difficulty of human motion parameters contour tracking; Secondly, Kalman random forest algorithm is used to estimate the global transformation of action and the different distance measures of body action in each sub-state by using action characteristics through boundary approximation and action sequence updating, so as to reduce the computational complexity of the algorithm; Finally, the single shape change and the dynamic action in motion are modeled, and the dynamic tracking of human motion is realized through the linear combination of human motion shape and style factor decomposition.

Machine-Learning-Based Cyclic Voltammetry Behavior Model for Supercapacitance of Co-Doped Ceria/rGO Nanocomposite.

This paper examines the cobalt-doped ceria/reduced graphene oxide (Co-CeO2/rGO) nanocomposite as a supercapacitor and modeling of its cyclic voltammetry behavior using Artificial Neural Network (ANN) and Random Forest Algorithm (RFA). Good agreement was found between experimental results and the predicted values generated by using ANN and RFA. Simulation results confirmed the accuracy of the models, compared to measurements from supercapacitor module power-cycling. A comparison of the best performance between ANN and RFA models shows that the ANN models performed better (value of coefficient of determination >0.95) than the RFA models for all datasets used in this study. The results of the ANN and RFA models could be useful in designing the unique nanocomposites for supercapacitors and other strategies related with energy and the environment.

First Year Clinical Parameters are Superior to that of the Pre-Treatment Results for Hepatocellular Carcinoma Prediction in Patient with Chronic Hepatitis B

Background: Although there are prediction models for hepatocellular carcinoma (HCC) in chronic hepatitis-B patients, post-treatment hepatitis prediction models on naive patients who started antiviral therapies are lacking. Also, it is not known at which point of the antiviral therapy the condition of the patient the predictive ability for HCC is increased. Method: This study enrolled 1333 naive patients with chronic hepatitis-B who are receiving initial treatment with antiviral therapy (entecavir or tenofovir). Data from 686 among those who had data and machine learning statistical methods were used to build a predictive model for HCC. Results: At 12 months after starting antiviral therapies, the indexes of blood tests were stabilized. Cirrhosis, AFP at 12 months, and platelet at 12 months were shown to be the most important variables. Among Logistic Regression, Decision Tree, Support Vector Machine, Random Forest algorithm models, Lasso logistic regression method showed the highest predictive ability (AUROC=0.843). When other predictive models for HCC were cross-validated using our data, high AUROC values were found (CU-HCC; 0.738, GAG-HCC; 0.787, REACH-B; 0.608, PAGE-B; 0.705, modified REACH-B; 0.742, APA-B; 0.798). Conclusion: In naive patients with chronic hepatitis-B, Cirrhosis, AFP, and platelet values one year since anti-virus treatment were superior at predicting HCC than pre-treatment indexes. Funding Statement: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea NRF-2017R1C1B5074215. Declaration of Interests: The authors declare no conflict of interest. Ethics Approval Statement: Approved by the IRB of each research institution (IRB: HYUH 2017-04-006).

Virtual Screening Techniques to Probe the Antimalarial Activity of some Traditionally Used Phytochemicals.

Malaria parasites show resistance to most of the antimalarial drugs and hence developing antimalarials which can act on multitargets rather than a single target will be a promising strategy of drug design. Here we report a new approach by which virtual screening of 292 unique phytochemicals present in 72 traditionally important herbs is used for finding out inhibitors of plasmepsin-2 and falcipain-2 for antimalarial activity against P. falciparum. Initial screenings of the selected molecules by Random Forest algorithm model of Weka using the bioassay datasets AID 504850 and AID 2302 screened 120 out of the total 292 phytochemicals to be active against the targets. Toxtree scan cautioned 21 compounds to be either carcinogenic or mutagenic and were thus removed for further analysis. Out of the remaining 99 compounds, only 46 compounds offered drug-likeness as per the 'rule of five' criteria. Out of ten antimalarial drug targets, only two target proteins such as 3BPF and 3PNR of falcipain-2 and 1PFZ and 2BJU of plasmepsin-2 are selected as targets. The potential binding of the selected 46 compounds to the active sites of these four targets was analyzed using MOE software. The docked conformations and the interactions with the binding pocket residues of the target proteins were understood by 'Ligplot' analysis. It has been found that 8 compounds are dual inhibitors of falcipain-2 and plasmepsin-2, with the best binding energies. Compound 117 (6aR, 12aS)-12a-Hydroxy-9-methoxy-2,3-dimethylenedioxy-8-prenylrotenone (Usaratenoid C) present in the plant Millettia usaramensis showed maximum molecular docking score.

Predicting postoperative complications of head and neck squamous cell carcinoma in elderly patients using random forest algorithm model.

BackgroundHead and Neck Squamous Cell Carcinoma (HNSCC) has a high incidence in elderly patients. The postoperative complications present great challenges within treatment and they're hard for early warning.MethodsData from 525 patients diagnosed with HNSCC including a training set (n = 513) and an external testing set (n = 12) in our institution between 2006 and 2011 was collected. Variables involved are general demographic characteristics, complications, disease and treatment given. Five data mining algorithms were firstly exploited to construct predictive models in the training set. Subsequently, cross-validation was used to compare the different performance of these models and the best data mining algorithm model was then selected to perform the prediction in an external testing set.ResultsData from 513 patients (age > 60 y) with HNSCC in a training set was included while 44 variables were selected (P < 0.05). Five predictive models were constructed; the model with 44 variables based on the Random Forest algorithm demonstrated the best accuracy (89.084 %) and the best AUC value (0.949). In an external testing set, the accuracy (83.333 %) and the AUC value (0.781) were obtained by using the random forest algorithm model.ConclusionsData mining should be a promising approach used for elderly patients with HNSCC to predict the probability of postoperative complications. Our results highlighted the potential of computational prediction of postoperative complications in elderly patients with HNSCC by using the random forest algorithm model.

A Data-Mining Approach to Monitoring Wind Turbines

The rapid expansion of wind farms has generated interest in operations and maintenance. An operating wind turbine undergoes various state changes, including transformation from a normal to a fault mode. Condition-based maintenance tools are needed to identify potential faults in the system. The prediction of turbine fault modes is of particular interest. In this research, data-mining algorithms are employed to construct prediction models for wind turbine faults. A three-stage prediction process is followed: 1) prediction of a fault of any kind; 2) prediction of specific faults of the system; and 3) identification on unseen faults. A comparative analysis of various data-mining algorithms is reported based on the data collected at a large wind farm. Random forest algorithm models provided the best accuracy among all algorithms tested. The robustness of the predictive model is validated for faults that have occurred at turbines with previously unseen data. The research results discussed in this paper have been derived from data collected at 17 wind turbines.