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A random forest algorithm based customer demand forecasting model for sports enterprises in the real economy

INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED DEVELOPMENT 2023年第2-4期13卷 363-378页

作者： Feng, Yingge Xu, Xiaowei Wang, Rongna Zhejiang Shuren Univ Sports Art & Lab Educ Ctr Hangzhou 310015 Peoples R China Zhejiang Shuren Univ Sch Humanities & foreign languages Hangzhou 310015 Peoples R China

The study proposes two strategies to optimise the random forest algorithm (RF) by fine-tuning the data distribution and introducing customer life values, constructing the improved random forest algorithm (IRF), and building a customer churn prediction model based on the IRF algorithm. A churn segmentation model is constructed based on the k-means algorithm to classify customers according to their characteristics in order to predict their needs and thus develop differentiated strategies to retain them. The experimental results show that the IRF prediction model has an accuracy of 99.84% and an AUC value of 0.932. The above results show that the accuracy of the IRF algorithm can meet the actual demand, which will contribute to the long-term development of sports enterprises. In the future, it is necessary to consider the relationship and interaction between customers to further improve the prediction accuracy of customer demand.

关键词： random forest algorithm customer churn data mining K-means algorithm customer lifetime value

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Application of stable isotopic and elemental composition combined with random forest algorithm for the botanical classification of Chinese honey

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2022年第0期110卷

作者： Liu, Zhaolong Xu, Tianyang Zhou, Jinhui Chen, Lanzhen Chinese Acad Agr Sci Inst Apicultural Res Beijing 100093 Peoples R China Minist Agr & Rural Affairs Key Lab Bee Prod Qual & Safety Control Beijing 100093 Peoples R China

To ensure that honey belongs to a very appreciated botanical class, the classical methodology is melissopalynology analysis to identify and count pollen grains. However, this method is time-consuming and laborious. In this work, four stable isotopes (delta(13)Ch, delta Cp-13, delta O-18 and delta H-2) and twelve elemental contents (Na, Mg, Ca, K, Fe, Cr, Mn, Co, Cu, Sr, Se, Mo) were used to build the dataset, and the random forest (RF) algorithm, Support Vector Machines (SVM), Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA) were investigated to classify six varieties of Chinese honey (linden, sunflower, vetch, rape, acacia, and jujube). The results showed the RF algorithm exhibits the highest training accuracy (99.4%) and testing accuracy (96.5%) of the four algorithms. Hence, the RF algorithm was selected to rank the 16 attributes according to their contribution, and delta H-2, Sr, delta O-18, Mn, CA, and K were comsidered the most important factors for identifying six varieties of honey. Furthermore, the results of the RF algorithm were verified by the parallel coordinates plot. This suggests that the RF algorithm provides an effective and accurate approach for classifying Chinese honey according to stable isotopic and elemental composition, which theoretically can be used to classify more types of honey.

关键词： random forest algorithm Chinese honey Botanical classification Stable isotopic Elemental composition Food analysis Food composition

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Development of a random forest based algorithm for road health monitoring

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 251卷

作者： Dugalam, Revanth Prakash, Guru IIT Indore Dept Civil Engn Indore 453552 MP India

Road damages, such as potholes and rutting, must be addressed in their early stages to prevent accidents and minimize maintenance costs. The presence of these damages not only causes discomfort to passengers but also accelerates vehicle deterioration. Improper material mixtures and inadequate maintenance practices often contribute to road damage. In recent years, numerous machine learning (ML) algorithms have been developed with a predominant focus on binary classification, specifically targeting potholes. However, roads are susceptible to various damages beyond potholes, necessitating a comprehensive approach for effective solutions. This study introduces a novel algorithm utilizing a random forest (RF) classifier for multi -class classification of road damages. The proposed algorithm is validated through rigorous simulation and field studies. Unlike previous models, this approach embraces a comprehensive perspective by considering a wider range of damages, thereby facilitating a more nuanced and inclusive process for the classification of road damages. In the field study, data has been collected using a set of uni-axial accelerometers and a smartphone camera mounted on a test vehicle. The data has been processed using the sliding window technique, and features have been extracted from each window to train the RF classifier. In this process, an optimal window size has been determined and employed to enhance the effectiveness of feature extraction for training the RF classifier. The proposed algorithm demonstrates significant accuracy in both simulation and field studies, with notable performance in identifying and classifying road damages. The algorithm's outcomes are utilized to estimate a detailed cost for repairing the identified damages. This paper showcases proficiency in addressing various damages, offering valuable insights for the implementation of cost-effective road maintenance strategies.

关键词： Road health monitoring random forest algorithm Road damages Vibration data Repair cost estimation

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Fault Diagnosis Method of Photovoltaic Array Based on random forest algorithm

Fault Diagnosis Method of Photovoltaic Array Based on Random...

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第三十九届中国控制会议

作者： Sizhe Gong Xunhao Wu Ziwen Zhang Northeastern University College of Information Science and Engineering

This paper proposes a fault diagnosis method for photovoltaic array based on random forest algorithm. The random forest algorithm used in this paper is based on classification regression tree(CART). And bootstrap sampling method was used to generate multiple training sets from the original training set, and random attributes were introduced in the training process to generate multiple CART trees. When troubleshooting, the final troubleshooting results are determined by voting according to the diagnosis results of each CART tree. By building a photovoltaic array model in Matlab/Simulink simulation module, use it to summarize the output characteristics of photovoltaic array in the common fault states(partial shading, abnormal component aging, open circuit, short circuit). And considering the main natural factors that affect the output of photovoltaic array, the open circuit voltage, short circuit current, maximum power point voltage, maximum power point current, environmental temperature and light irradiance were selected as the input parameters of the random forest algorithm. Then, a 2×2 small photovoltaic array experiment platform and a field data acquisition system were designed and built to collect data in order to further verify the feasibility of the random forest algorithm. The test results of random forest were compared with those of single CART decision tree, K nearest neighbor(KNN) and support vector machine(SVM) algorithms. The results showed that random forest could complete the fault diagnosis task of photovoltaic array better.

关键词： photovoltaic array fault diagnosis random forest algorithm Matlab/Simulink simulation photovoltaic array experimental platform

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Financial Data Sharing System for State-owned Enterprises based on random forest algorithm

Financial Data Sharing System for State-owned Enterprises ba...

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2022年第六届国际科技创新与教育发展学术会议

作者： Xiaotao Chang University of San Carlos

With the development of social science and technology, information technology has been gradually applied to various fields and played a key role in the development of enterprises, while as a state-owned enterprise, under the background of the continuous development of information technology, the business involved has become more complex and its scale has been increasing, and the scope of application of the financial data sharing system for state-owned enterprises is also expanding. At the same time, the requirements of enterprise managers for the timeliness, comprehensiveness and accuracy of financial information are subsequently increasing, and the traditional financial management can no longer meet the current needs. Therefore, it is necessary to promote the information construction of local state-owned enterprises in financial management, improve the level of financial information management, enhance the competitiveness of state-owned enterprises, and stabilize the position of state-owned enterprises in the market. The random forest algorithm can be used in the financial data sharing system of state-owned enterprises.

关键词： random forest algorithm State-owned enterprises Financial data Sharing system

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Identification of SARS-CoV-2 origin: Using Ngrams, principal component analysis and random forest algorithm

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Informatics in Medicine Unlocked 2021年 24卷 100577页

作者： El Boujnouni, Hamoucha Rahouti, Mohamed El Boujnouni, Mohamed Research Center of Plant and Microbial Biotechnologies Biodiversity and Environment Faculty of Sciences Mohammed V University in Rabat PO Box 1014 Morocco Laboratory of Information Technologies National School of Applied Sciences Chouaib Doukkali University in El Jadida PO Box 1166 Morocco

COVID-19 is an infectious disease caused by the newly discovered SARS-CoV-2 virus. This virus causes a respiratory tract infection, symptoms include dry cough, fever, tiredness and in more severe cases, breathing difficulty. SARS-CoV-2 is an extremely contagious virus that is spreading rapidly all over the world and the scientific community is working tirelessly to find an effective treatment. This paper aims to determine the origin of this virus by comparing its nucleic acid sequence with all members of the coronaviridae family. This study uses a new approach based on the combination of three powerful techniques which are: Ngrams (For text categorization), Principal Component Analysis (For dimensionality reduction) and random forest algorithm (For supervised classification). The experimental results have shown that a large set of SARS-CoV-2 genomes, collected from different locations around the world, present significant similarities to those found in pangolins. This finding confirms some previous results obtained by other methods, which also suggest that pangolins should be considered as possible hosts in the emergence of the new coronavirus. © 2021 The Authors

关键词： Bioinformatics COVID-19 Genomes Ngrams Principal component analysis random forest algorithm SARS-CoV-2

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Research on Fault Determination of Active Distribution Network Based on random forest-SVM algorithm 3rd

Research on Fault Determination of Active Distribution Netwo...

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3rd International Symposium on New Energy and Electrical Technology (ISNEET)

作者： Zhao, Wei Xiao, Shiwu North China Elect Power Univ Sch Elect Power & Elect Engn State Key Lab New Energy Beijing Peoples R China

ISBN: (纸本)9789819905553;9789819905539;9789819905522

After the distributed generation supply is connected to the traditional distribution network, the traditional distribution network changes from "passive" to "active", and the power flowchanges from one-way to two-way. When the active distribution network fails, the short-circuit current is small, inconspicuous fault characteristics and rich harmonics make it difficult to locate faults. In this paper, a method for locating fault sections of active distribution network based on random forest-SVM algorithm is proposed. The method first performs fast Fourier transform on the voltage information collected at each measurement point to form the fundamental frequency voltage of each measurement point. And the fault feature set of the 2-7th harmonic voltage, the feature importance evaluation and feature selection of each fault feature are carried out by the random forest algorithm, and finally the fault section is located by the SVMalgorithm. By connecting distributed generation and nonlinear loads in the IEEE33 node distribution network, the simulation results show that the method has rapidity, accuracy and fault tolerance for fault section location.

关键词： Component Active distribution network Fault location location random forest algorithm SVM algorithm

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Improvement of random forest Ensembling algorithm Efficiency Through Cardinal Tuning of n_ estimators Parameter

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JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2024年第1-3期42卷 67-93页

作者： Sandhu, Gaurav Singh, Amandeep Bedi, Parmiderpal Singh Lamba, Puneet Singh Chaudhary, Gopal GGSIPU Guru Tegh Bahadur Inst Technol New Delhi India Univ Delhi SGTB Khalsa Coll Dept Comp Sci Delhi 110007 India VIPS TC Sch Engn & Technol New Delhi India

In today's world multiple type of datasets are available, they are mainly divided into two type namely classification and regression;based on the dataset type machine learning models are applied. There is a technique called bagging in which multiple models are trained simultaneously and produce results on testing dataset. These results are then clubbed to vote in the majority of the same outputs produced. This output is then considered as final output. The bagging technique used in this study is random forest (RF) algorithm, where the multiple models are replaced with decision trees. The RF algorithm requires multiple parameters to produce optimum out-put. These parameters are n_ estimators, max_ samples, max_ features etc. Though RF algorithm produces favourable results with default values of parameters, but to improve the efficiency of RF algorithm tuning of param-eters is preferred. In this study, parameter n_ estimators is tuned, on basis of the length of dataset to produce improved results as compared to the default parameters value. Further the proposed tuning method has been applied to RF algorithm and improvement of class prediction efficiency on different datasets is measured in terms of accuracy, precision, recall, F1-score. Also, implementation of ROC curves and auc is performed to depict the improvement of RF algorithm for datasets under consideration,

关键词： Classification regression random forest algorithm bagging, decision trees n_ estimators max_ samples max_ features accuracy, precision recall F1-score ROC auc

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Introduced a new method for enhancement of intrusion detection with random forest and PSO algorithm

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SECURITY AND PRIVACY 2021年第2期4卷

作者： Ajdani, Mahdi Ghaffary, Hamidreza Islamic Azad Univ Dept Comp Sci Ferdows Branch Ferdows Iran

As computer networks expand, attacks and intrusions into these networks have increased. In addition to firewalls and other intrusion prevention equipment, other systems, such as IDS (Metrics), are designed to provide enhanced security in computer systems, including the purpose of monitoring intrusive and intrusive activities. Intrusive allocation system can be considered effective if the high intrusion rate is slightly misleading, and in this article a new way to classify it is abnormal (infiltration) in the host or network. UNSW-NB15 and KDD-Cup'99 Datasets Introducing random forest and PSO algorithm. In this paper, training data and label data used with the random forest algorithm. After creating a random forest algorithm, provide test data. We use the data stored in train step, which is actually a copy of the data, so that when performing the test step, the same training data can be compared with categorize using a PSO algorithm. In in order to show the accuracy of the proposed method, an example of the confusion matrix formed in the code that showed performance of all methods and modes studied is compared based on accuracy and time that the PSO algorithm has always been able to take less time, which is quite acceptable and predictable. Improves correct diagnosis of correct detection rate in that report was 75.94% and in the proposed method in this article it reached 97%. With the proposed method, learning speed has been greatly increased and accuracy is acceptable.

关键词： behavioral pattern allotment data mining PSO algorithm random forest algorithm

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Collaborative Optimization on Both Weight and Fatigue Life of Fifth Wheel Based on Hybrid random forest with Improved BP algorithm

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APPLIED SCIENCES-BASEL 2025年第7期15卷 4006-4006页

作者： Xue, Huan Guo, Chang Peng, Xiaojian Xu, Saiqing Li, Kaixian Li, Jianwen Hubei Univ Technol Dept Mech Engn Wuhan 430068 Peoples R China Hubei Univ Technol Hubei Key Lab Modern Mfg Qual Engn Wuhan 430068 Peoples R China

The fifth wheel of the semi-trailer tractor is a key component connecting the tractor and the semi-trailer. During operation, the fifth wheel experiences frequent irregular and repetitive loading conditions. This leads to a decline in its durability and fatigue life, which can significantly impact the efficiency of cargo transport. The lightweight design enhances both the transport efficiency and fuel economy of the semi-trailer tractor. In this research, to achieve weight reduction while maintaining the wear-resistant failure protection performance in semi-trailer tractors, we selected a new material-special steel for saddles (SD600). Its stress-strain and fatigue life were analyzed under static compression, uphill lifting, and steering rollover conditions. These findings confirm the necessity of implementing lightweighting measures. Using a multi-objective genetic algorithm, we established an optimization model aimed at balancing weight reduction and fatigue life enhancement. As a result, the optimized fifth wheel achieved a 24.11% reduction in mass, while its fatigue life increased by 15 times, thus realizing the synergistic optimization of weight and fatigue life. We proposed a prediction model combining a random forest algorithm with an optimized back propagation (BP) neural network. Compared to the traditional BP approach, this model improved the mean absolute percentage error (MAPE) by 47.62%. Quadratic optimization was conducted based on the optimal design option set, using data analysis to determine the range of values of each variable under specific constraints and to verify the stress-strain and fatigue life for very small values in the range.

关键词： fifth wheel lightweight fatigue life random forest algorithm BP neural network

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