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An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm

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JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2022年第2期5卷 629-659页

作者： Shin, Tacksoo Long, Jeffrey D. Davison, Mark L. Myongji Univ Dept Youth Educ & Leadership Seoul South Korea Univ Iowa Dept Psychiat Iowa City IA 52242 USA Univ Minnesota Twin Cities Dept Educ Psychol Minneapolis MN USA

The objective of this study was to examine the performance of the two most popular missing data methods (i.e., multiple imputation and maximum likelihood), as well as newly developed machine learning framework based on random forest algorithm for missing data under various reserach conditions. The design of the simulation study included random and non-random missingness (i.e., MCAR, MAR, and MNAR), small samples, and different levels of missing rates. All statistical inferences were investigated using latent variable interaction modeling. Consistent with the missing data literature, the combined effects of small sample sizes, higher missing rates, and non-ignorable missingness along with complicated modeling structure adversely affected the accuracy of statistical inferences. Although there is a possibility for overparameterization, it is a good way to select MI when convergence is concerned. If the primary goal of research is to investigate the relationship between variables as in many studies, ML would be attractive. MF presented similar performance compared to MI and ML across all research conditions and outperformed when estimating the variability of parameter estimates. Other practical issues pertaining to the missing data methods were also discussed.

关键词： Missing data Multiple imputation Maximum likelihood estimation random forest algorithm Latent interaction model

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Fault Diagnosis Method of Photovoltaic Array Based on random forest algorithm 39

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

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39th Chinese Control Conference (CCC)

作者： Gong, Sizhe Wu, Xunhao Zhang, Ziwen Northeastern Univ Coll Informat Sci & Engn Shenyang 110000 Peoples R China

ISBN: (纸本)9789881563903

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 2X2 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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Research on Non-Intrusive Load Monitoring Based on random forest algorithm 4

Research on Non-Intrusive Load Monitoring Based on Random Fo...

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4th International Conference on Smart Grid and Smart Cities (ICSGSC)

作者： Wu Guohua Yuan Diping Yin Jiyao Zeng Wenhua Deng Peng Xiao Yiqing Harbin Inst Technol Shenzhen 518000 Peoples R China Shenzheng Urban Publ Safety & Technol Inst Shenzhen 50080 Guangdong Peoples R China

ISBN: (数字)9781728194042

ISBN: (纸本)9781728194042

It is of great significance for load monitoring to monitor illegal use of electricity. Load monitoring can provide supervision for government and improve residents' awareness of safety. Compared with the intrusive load monitoring, non-intrusive load monitoring has the advantages of good economy, high reliability, and quickly realizing the electricity decomposition. Many scholars have carried out this research, but there still exists the problems: it is difficult to obtain key information from big data and the diagnostic results are inaccuracy. Therefore, load monitoring is conducted in this paper. To overcome the above shortcomings, firstly this paper obtains key information of sample based on harmonic analysis. Secondly, random forest based on multiple decision trees has better accuracy in recognition. So the random forest algorithm is applied to machine learning and pattern recognition. Finally, the proposed method is identified and analyzed. The results show that the accuracy of the online electrical detection based on the harmonic analysis and random forest algorithm is greater than 86%, which shows the effectiveness of the method.

关键词： non-intrusive load monitoring harmonic analysis random forest algorithm

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A Scalable random forest algorithm Based on MapReduce

A Scalable Random Forest Algorithm Based on MapReduce

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2013 IEEE 4th International Conference on Software Engineering and Service Science

作者： Jiawei Han Yanheng Liu Xin Sun College of Computer Science and Technology Jilin University College of Computer Science and Technology Changchun University

ISBN: (纸本)9781467349994

random forest is a popular data classification algorithm for machine *** paper proposes SMRF algorithm-an improved scalable random forest algorithm based on Map Reduce *** new algorithm makes data classification in computer cluster or cloud computing environment for massive *** processes and optimizes the subsets of the data across multiple participating computing nodes by *** experimental results show that the SMRF algorithm has the equally accuracy degradation but higher performance while comparing with traditional random forest *** algorithm is more suitable to classify massive data sets in distributing computing environment than traditional random forest algorithm.

关键词： component random forest algorithm Map-Reduce data classification

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A Method to Detect Machine Generated Domain Names Based on random forest algorithm

A Method to Detect Machine Generated Domain Names Based on R...

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International Conference on Information System and Artificial Intelligence (ISAI)

作者： Song, Wen-Jie Li, Bin NARI Grp Corp Ctr Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509015856

In this paper, we proposed a method to detect machine generated domain names. Differences between the white list and the black list were analyzed to build a feature template from the angle of lexicology and linguistics. In contrast to the method based on the similarity, we adopted random forest algorithm to meet the demand of actual detection system by improving the generalization ability. Experimental results show that the method can not only achieve good performance in the efficiency and accuracy of detection, but also provide effective information support for security incidents.

关键词： domain generation algorithm random forest algorithm N-Gram model

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Analysis of Neural Network algorithm in Comparison to Multiple Linear Regression and random forest algorithm

Analysis of Neural Network Algorithm in Comparison to Multip...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Meghana, Pagolu Annepu, Visalakshi Jweeg, Muhsin Jaber Bagadi, Kalapraveen Aljibori, H.S.S. Mohammed, M.N. Abdullah, Oday I. Aldulaimi, Saeed Alfiras, M. School of Comp. Sci. and Engineering VIT-AP University Amravati India College of Technical Engineering AI-F Farahidi University Baghdad Iraq School of Electronics Engineering VIT-AP University Amravati India Al-Warith Center for Crowd Engineering and Management Research University of Warith AI-Anbiyaa Karbala Iraq Gulf University Mechanical Engineering Department Sanad Bahrain University of Baghdad Dept of Energy Engineering Iraq College of Engineering Gulf University Mechanical Engineering Dept Sanad26489 Bahrain Manama Bahrain College of Engineering Gulf University Electrical and Electronic Engineering Department Sanad26489 Bahrain

ISBN: (纸本)9798350372229

Regression analysis, a stalwart in statistical methodology, offers a robust framework for predicting outcomes based on historical data. It hinges on the premise that by scrutinizing past input data, one can discern the relationships between independent and dependent variables, enabling the forecasting of final results. In the dynamic landscape of Machine Learning, a multitude of regression techniques exists. Nevertheless, many real-world companies grapple with optimizing their return on investment due to the perplexing task of selecting the most apt model for their specific datasets. This research endeavor seeks to bridge this knowledge gap by conducting a comprehensive comparative analysis of three widely used and highly proficient regression algorithms: Multiple Linear Regression (MLR), random forest (RF), and Neural Networks (NNs). MLR offers a simple and interpretable linear model, while RF harnesses ensemble learning to handle complex relationships, and NN s employ intricate, nonlinear modeling capabilities. The study subjects two distinct datasets, Crop Yield, and Cardiovascular Disease, to scrutiny. The former addresses agricultural productivity forecasting, while the latter explores healthcare applications. By evaluating these datasets using the three regression models, the research aims to determine the most suitable model for each dataset's unique characteristics, enabling data-driven decision-making and enhancing the efficacy of regression analysis in practical, real-world scenarios. © 2024 IEEE.

关键词： Data set for prediction Multiple Linear Regression Neural Networks random forest algorithm Regression

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Hybrid Approach to Improving the Results of the SVM Classification Using the random forest algorithm 13

Hybrid Approach to Improving the Results of the SVM Classifi...

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13th International Symposium on Intelligent Systems (INTELS)

作者： Demidova, L. A. Klyueva, I. A. Pylkin, A. N. Ryazan State Radio Engn Univ 59-1 Gagarina Str Ryazan 390005 Russia

The aim of this work is to improve the results of the SVM classification (Support Vector Machine) by hybridizing the SVM classifier with the random forest classifier (random forest, RF) used as the auxiliary. Specification of the classification decisions obtained on the basis of the SVM classifier is performed for the objects located in the experimentally determined subareas near the hyperplane separating the classes and including both correctly and erroneously classified objects. In the case of improving the quality of the objects classification from the initial dataset, the proposed hybrid approach to the objects classification can be recommended for classification of new objects. When developing the SVM classifier, the fixed default parameters values are used. A comparative analysis of the classification results obtained during the computational experiments in the hybridization of the SVM classifier with two auxiliary classifiers the random forest classifier (RF classifier) and the k nearest neighbor classifier (kNN classifier), for which the parameters values are determined randomly, confirms the expediency of using of these classifiers to increase the SVM classification quality. It was found that in most cases, the random forest classifier works better in terms of improving the SVM classification quality in comparison with the kNN classifier. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： intelligent classification algorithm SVM algorithm k-nearest neighbors algorithm random forest algorithm support vector kernel function kernel function parameter regularization parameter hybridization

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Analysis of Precision Agriculture based on random forest algorithm by using Sensor Networks 5

Analysis of Precision Agriculture based on Random Forest Alg...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Pavithra, K. Jayalakshmi, M. Natl Engn Coll Comp Sci & Engn Kovilpatti Tamil Nadu India

ISBN: (纸本)9781728146850

India is the largest country where most of the human lives depends upon agriculture. Due to the shortage of water facilities, the yield of irrigation is affected. To reduce this shortage of water, different types of methods are invented to supply the correct level of water to the agricultural lands. Here precision irrigation system is used to maintain the perfect water supply from this method wastage of water can be reduced. By using this technique, we can achieve better harvesting results. The total amount of water for lands and plants can be calculated by using the PH value. To detect the PH level, a random forest algorithm is used. There is a direct relationship between the number of plants in the land and the results it can get. It improves the prediction accuracy.

关键词： Irrigation Precision Agriculture random forest algorithm Water Prediction

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Multilevel random forest algorithm in image recognition for various scientific applications

Multilevel Random forest algorithm in image recognition for ...

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73rd IEEE National Aerospace and Electronics Conference (NAECON)

作者： Karmakar, Sayantani Karmakar, Supriya Portland State Univ Portland OR 97207 USA SUNY Farmingdale State Coll Farmingdale NY 11735 USA

ISBN: (纸本)9781665448598

Artificial Intelligence (AI) is a field of research where researchers and scientists are trying to understand our nature and incorporate that understanding in the machine and computer to serve our society. Machine learning (ML) is another aspect of AI. Several algorithms are inverted, modified and updated to characterize the understanding of the nature and implement intelligence artificially within the machine. One major challenge of AI is to understand the object. The first identification of an object is visual information. There are several mechanisms and algorithms to identify an object. Every algorithm has some pros and cons. Among these algorithms, decision tree and random forest algorithm, which is a combination of decision tree algorithms, can play a very effective role. This work shows the application of the multilevel random forest algorithm (MRFA) which is a modified application of the random forest algorithm is introduced for image classification.

关键词： Artificial Intelligence (AI) Image classification Decision tree random forest algorithm Multilevel random forest algorithm (MRFA) Machine Learning (ML)

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ENSEMBLE CLASSIFIER WITH random forest algorithm TO DEAL WITH IMBALANCED HEALTHCARE DATA

ENSEMBLE CLASSIFIER WITH RANDOM FOREST ALGORITHM TO DEAL WIT...

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International Conference on Information Communication and Embedded Systems (ICICES)

作者： Anbarasi, M. S. Janani, V. Pondicherry Engn Coll Dept Informat & Technol Pondicherry 605014 India

ISBN: (纸本)9781509061358

In day today life, data is generated in massive amount with rapid growth in health care environment. The medical industries have large amount of data sets, for diagnosis purpose and maintain patient's records. The medical researches come with new treatments and medicine every day. But availability of medical datasets is often not balanced in their class labels. The performance of some existing method is poor on imbalanced dataset. So the prediction of disease from imbalanced data becomes difficult to handle. In this proposal Classifier ensemble method (random forest algorithm) can be used to overcome existing classifier techniques. Multiple classifier system is more accurate and robust than an existing classifier technique. The ensemble method proves to be very efficient in classification of records from available imbalanced healthcare patient data, as it involves the process of considering opinion from multiple base classifiers, as opposed to the single classifier method. This method gives a very accurate and precise inference, as unrelated data's are removed because of multiple base classifiers. The problems of healthcare dataset especially with some uncertainty can be predicted.

关键词： Ensemble Classifier random forest algorithm Data pre-processing Anomaly Detection Technique Clustering technique

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