Thematische Bibliographien / Pruning random forest

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Auswahl der wissenschaftlichen Literatur zum Thema „Pruning random forest“

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Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "Pruning random forest"

Yang, Fan, Wei-hang Lu, Lin-kai Luo und Tao Li. „Margin optimization based pruning for random forest“. Neurocomputing 94 (Oktober 2012): 54–63. http://dx.doi.org/10.1016/j.neucom.2012.04.007.

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Tarchoune, Ilhem, Akila Djebbar und Hayet Farida Merouani. „Improving Random Forest with Pre-pruning technique for Binary classification“. All Sciences Abstracts 1, Nr. 2 (25.07.2023): 11. http://dx.doi.org/10.59287/as-abstracts.1202.

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Random Forest (RF) is a popular machine learning algorithm. It is based on the concept of ensemble learning, which is a process of combining several classifiers to solve a complex problem and improve model performance. The random forest allows extending the notions of decision trees (DT) in order to build more stable models. In this work we propose to further improve the predictions of the trees in the forest by a pre-pruning technique, which aims to optimize the performance of the nodes and to minimize the size of the trees. Two experiments are performed to evaluate the performance of the proposed method; in the first experiment we applied the Classical Random Forest algorithm (CRF) with several different trees. While in the second one, a pre-pruning technique is established on the trees in order to define the optimal size of the forest. Finally, we compared the results obtained. The main objective is to produce accurate decision trees with high precision. The effectiveness of the proposed method is validated on five medical databases; the prediction precision will be improved with 83%, 94%, 95%, 97%, and 81% for Diabetes, Hepatitis, SaHeart, EEG-Eye-State, Prostate-cancer databases respectively. The performance results confirm that the proposed method performs better than the classical random forest algorithm.

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Fawagreh, Khaled, und Mohamed Medhat Gaber. „eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning“. Big Data and Cognitive Computing 4, Nr. 4 (28.11.2020): 37. http://dx.doi.org/10.3390/bdcc4040037.

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To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and are being developed to provide, using mobile and electronic technology, higher diagnosis quality of the diseases, better treatment of the patients, and improved quality of lives. Since smart healthcare applications that are mainly concerned with the prediction of healthcare data (like diseases for example) rely on predictive healthcare data analytics, it is imperative for such predictive healthcare data analytics to be as accurate as possible. In this paper, we will exploit supervised machine learning methods in classification and regression to improve the performance of the traditional Random Forest on healthcare datasets, both in terms of accuracy and classification/regression speed, in order to produce an effective and efficient smart healthcare application, which we have termed eGAP. eGAP uses the evolutionary game theoretic approach replicator dynamics to evolve a Random Forest ensemble. Trees of high resemblance in an initial Random Forest are clustered, and then clusters grow and shrink by adding and removing trees using replicator dynamics, according to the predictive accuracy of each subforest represented by a cluster of trees. All clusters have an initial number of trees that is equal to the number of trees in the smallest cluster. Cluster growth is performed using trees that are not initially sampled. The speed and accuracy of the proposed method have been demonstrated by an experimental study on 10 classification and 10 regression medical datasets.

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El Habib Daho, Mostafa, Nesma Settouti, Mohammed El Amine Bechar, Amina Boublenza und Mohammed Amine Chikh. „A new correlation-based approach for ensemble selection in random forests“. International Journal of Intelligent Computing and Cybernetics 14, Nr. 2 (23.03.2021): 251–68. http://dx.doi.org/10.1108/ijicc-10-2020-0147.

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PurposeEnsemble methods have been widely used in the field of pattern recognition due to the difficulty of finding a single classifier that performs well on a wide variety of problems. Despite the effectiveness of these techniques, studies have shown that ensemble methods generate a large number of hypotheses and that contain redundant classifiers in most cases. Several works proposed in the state of the art attempt to reduce all hypotheses without affecting performance.Design/methodology/approachIn this work, the authors are proposing a pruning method that takes into consideration the correlation between classifiers/classes and each classifier with the rest of the set. The authors have used the random forest algorithm as trees-based ensemble classifiers and the pruning was made by a technique inspired by the CFS (correlation feature selection) algorithm.FindingsThe proposed method CES (correlation-based Ensemble Selection) was evaluated on ten datasets from the UCI machine learning repository, and the performances were compared to six ensemble pruning techniques. The results showed that our proposed pruning method selects a small ensemble in a smaller amount of time while improving classification rates compared to the state-of-the-art methods.Originality/valueCES is a new ordering-based method that uses the CFS algorithm. CES selects, in a short time, a small sub-ensemble that outperforms results obtained from the whole forest and the other state-of-the-art techniques used in this study.

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Gefeller, Olaf, Asma Gul, Folkert Horn, Zardad Khan, Berthold Lausen und Werner Adler. „Ensemble Pruning for Glaucoma Detection in an Unbalanced Data Set“. Methods of Information in Medicine 55, Nr. 06 (2016): 557–63. http://dx.doi.org/10.3414/me16-01-0055.

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SummaryBackground: Random forests are successful classifier ensemble methods consisting of typically 100 to 1000 classification trees. Ensemble pruning techniques reduce the computational cost, especially the memory demand, of random forests by reducing the number of trees without relevant loss of performance or even with increased perfor -mance of the sub-ensemble. The application to the problem of an early detection of glaucoma, a severe eye disease with low prevalence, based on topographical measurements of the eye background faces specific challenges.Objectives: We examine the performance of ensemble pruning strategies for glaucoma detection in an unbalanced data situation.Methods: The data set consists of 102 topo-graphical features of the eye background of 254 healthy controls and 55 glaucoma patients. We compare the area under the receiver operating characteristic curve (AUC), and the Brier score on the total data set, in the majority class, and in the minority class of pruned random forest ensembles obtained with strategies based on the prediction accuracy of greedily grown sub-ensembles, the uncertainty weighted accuracy, and the similarity between single trees. To validate the findings and to examine the influence of the prevalence of glaucoma in the data set, we additionally perform a simulation study with lower prevalences of glaucoma.Results: In glaucoma classification all three pruning strategies lead to improved AUC and smaller Brier scores on the total data set with sub-ensembles as small as 30 to 80 trees compared to the classification results obtained with the full ensemble consisting of 1000 trees. In the simulation study, we were able to show that the prevalence of glaucoma is a critical factor and lower prevalence decreases the performance of our pruning strategies.Conclusions: The memory demand for glaucoma classification in an unbalanced data situation based on random forests could effectively be reduced by the application of pruning strategies without loss of perfor -mance in a population with increased risk of glaucoma.

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Zhu, Wancai, Zhaogang Liu, Weiwei Jia und Dandan Li. „Modelling the Tree Height, Crown Base Height, and Effective Crown Height of Pinus koraiensis Plantations Based on Knot Analysis“. Forests 12, Nr. 12 (15.12.2021): 1778. http://dx.doi.org/10.3390/f12121778.

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Taking 1735 Pinus koraiensis knots in Mengjiagang Forest Farm plantations in Jiamusi City, Heilongjiang Province as the research object, a dynamic tree height, effective crown height, and crown base height growth model was developed using 349 screened knots. The Richards equation was selected as the basic model to develop a crown base height and effective crown height nonlinear mixed-effects model considering random tree-level effects. Model parameters were estimated with the non-liner mixed effect model (NLMIXED) Statistical Analysis System (SAS) module. The akaike information criterion (AIC), bayesian information criterion (BIC), −2 Log likelihood (−2LL), adjusted coefficient (Ra2), root mean square error (RMSE), and residual squared sum (RSS) values were used for the optimal model selection and performance evaluation. When tested with independent sample data, the mixed-effects model tree effects-considering outperformed the traditional model regarding their goodness of fit and validation; the two-parameter mixed-effects model outperformed the one-parameter model. Pinus koraiensis pruning times and intensities were calculated using the developed model. The difference between the effective crown and crown base heights was 1.01 m at the 15th year; thus, artificial pruning could occur. Initial pruning was performed with a 1.01 m intensity in the 15th year. Five pruning were required throughout the young forest period; the average pruning intensity was 1.46 m. The pruning interval did not differ extensively in the half-mature forest period, while the intensity decreased significantly. The final pruning intensity was only 0.34 m.

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Paudel, Nawaraj, und Jagdish Bhatta. „Mushroom Classification using Random Forest and REP Tree Classifiers“. Nepal Journal of Mathematical Sciences 3, Nr. 1 (31.08.2022): 111–16. http://dx.doi.org/10.3126/njmathsci.v3i1.44130.

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Mushroom is a popular fruit of a much larger fungus that has a high level of protein and a rich source of vitamin B. It aids in the prevention of cancer, weight loss, and immune system enhancement. There are numerous thousands of mushroom species within the world and a few are eatable and a few are noxious due to noteworthy poisons on them. Hence, it is a vital errand to distinguish between eatable and harmful mushrooms. This paper focuses on comparing the performance of Random Forest and Reduced Error Pruning (REP) Tree classification algorithms for the classification of edible and poisonous mushrooms. In this paper, mushroom dataset from UCI machine learning repository has been classified using Random Forest and REP Tree classifiers. The result based on accuracy, precision, recall and F-measure showed that the Random Forest outperformed REP Tree algorithm as it had highest accuracy value of 100%, precision value of 100%, recall value of 100% and F- measure value of 100%. The performance is 100% by using Random Forest, which is found better with respect to REP Tree classifier.

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Yadav, Dhyan Chandra, und Saurabh Pal. „Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques“. International Journal of Big Data and Analytics in Healthcare 6, Nr. 1 (Januar 2021): 40–56. http://dx.doi.org/10.4018/ijbdah.20210101.oa4.

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This paper has organized a heart disease-related dataset from UCI repository. The organized dataset describes variables correlations with class-level target variables. This experiment has analyzed the variables by different machine learning algorithms. The authors have considered prediction-based previous work and finds some machine learning algorithms did not properly work or do not cover 100% classification accuracy with overfitting, underfitting, noisy data, residual errors on base level decision tree. This research has used Pearson correlation and chi-square features selection-based algorithms for heart disease attributes correlation strength. The main objective of this research to achieved highest classification accuracy with fewer errors. So, the authors have used parallel and sequential ensemble methods to reduce above drawback in prediction. The parallel and serial ensemble methods were organized by J48 algorithm, reduced error pruning, and decision stump algorithm decision tree-based algorithms. This paper has used random forest ensemble method for parallel randomly selection in prediction and various sequential ensemble methods such as AdaBoost, Gradient Boosting, and XGBoost Meta classifiers. In this paper, the experiment divides into two parts: The first part deals with J48, reduced error pruning and decision stump and generated a random forest ensemble method. This parallel ensemble method calculated high classification accuracy 100% with low error. The second part of the experiment deals with J48, reduced error pruning, and decision stump with three sequential ensemble methods, namely AdaBoostM1, XG Boost, and Gradient Boosting. The XG Boost ensemble method calculated better results or high classification accuracy and low error compare to AdaBoostM1 and Gradient Boosting ensemble methods. The XG Boost ensemble method calculated 98.05% classification accuracy, but random forest ensemble method calculated high classification accuracy 100% with low error.

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González, Sergio, Francisco Herrera und Salvador García. „Monotonic Random Forest with an Ensemble Pruning Mechanism based on the Degree of Monotonicity“. New Generation Computing 33, Nr. 4 (Juli 2015): 367–88. http://dx.doi.org/10.1007/s00354-015-0402-4.

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Mulyo, Harminto, und Nadia Annisa Maori. „PENINGKATAN AKURASI PREDIKSI PEMILIHAN PROGRAM STUDI CALON MAHASISWA BARU MELALUI OPTIMASI ALGORITMA DECISION TREE DENGAN TEKNIK PRUNING DAN ENSEMBLE“. Jurnal Disprotek 15, Nr. 1 (02.01.2024): 15–25. http://dx.doi.org/10.34001/jdpt.v15i1.5585.

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ENHACING PREDICTION ACCURACY OF NEW STUDENT PROGRAM SELECTION THROUGH DECISION TREE ALGORITHM OPTIMIZATION WITH PRUNING TECHNIQUE AND ENSEMBLEIn the current era of reform and globalization, the complexity of choosing the right study program is increasing with the many choices available. One of the challenges faced by the Nahdlatul Ulama Islamic University (UNISNU) Jepara is the increase in students with non-active status which can have an impact on the reputation of the university. One of the factors that can influence is the inaccuracy of students in choosing a study program, so that they are reluctant to continue because they are not enthusiastic about continuing their studies. The solution provided is to predict the selection of the right study program for prospective new students by utilizing the Decision Tree algorithm which is optimized with pruning and ensemble techniques with Random Forest which can help overcome overfitting in the decision tree. The data used is UNISNU student data from 2013 to 2023 with a total of 15,289 records and 52 attributes. The results showed that the Decision Tree and Random Forest models provided the highest accuracy, namely 0.88 with a max_depth value of 20 and succeeded in overcoming the problem of overfitting the decision tree. This model can then be used as a recommendation in predicting the selection of study programs for prospective new students at UNISNU Jepara.Dalam era reformasi dan globalisasi saat ini, kompleksitas dalam memilih program studi yang sesuai semakin meningkat dengan banyaknya pilihan yang tersedia. Salah satu tantangan yang dihadapi oleh Universitas Islam Nahdlatul Ulama (UNISNU) Jepara adalah meningkatnya mahasiswa dengan status non-aktif yang dapat berdampak pada reputasi universitas. Salah satu faktor yang dapat mempengaruhi adalah ketidaktepatan mahasiswa dalam memilih program studi, sehingga enggan untuk meneruskan karena tidak bersemangat dalam melanjutkan perkuliahan. Solusi yang diberikan adalah dengan melakukan prediksi pemilihan program studi bagi yang tepat bagi calon mahasiswa baru dengan memanfaatkan algoritma Decision Tree yang dioptimalkan dengan teknik pruning dan ensemble dengan Random Forest yang dapat membantu mengatasi overfitting pada decision tree. Data yang digunakan adalah data mahasiswa UNISNU dari tahun 2013 sampai dengan 2023 dengan jumlah 15.289 record dan 52 atribut. Hasil penelitian menunjukkan model Decision Tree dan Random Forest memberikan akurasi tertinggi, yaitu 0.88 dengan nilai max_depth sebesar 20 dan berhasil mengatasi masalah overfitting pada decision tree. Model ini selanjutnya dapat menjadi rekomendasi dalam prediksi pemilihan program studi bagi calon mahasiswa baru di UNISNU Jepara.

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Dissertationen zum Thema "Pruning random forest"

Diyar, Jamal. „Post-Pruning of Random Forests“. Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15904.

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Abstract Context. In machine learning, ensemble methods continue to receive increased attention. Since machine learning approaches that generate a single classifier or predictor have shown limited capabilities in some contexts, ensemble methods are used to yield better predictive performance. One of the most interesting and effective ensemble algorithms that have been introduced in recent years is Random Forests. A common approach to ensure that Random Forests can achieve a high predictive accuracy is to use a large number of trees. If the predictive accuracy is to be increased with a higher number of trees, this will result in a more complex model, which may be more difficult to interpret or analyse. In addition, the generation of an increased number of trees results in higher computational power and memory requirements. Objectives. This thesis explores automatic simplification of Random Forest models via post-pruning as a means to reduce the size of the model and increase interpretability while retaining or increasing predictive accuracy. The aim of the thesis is twofold. First, it compares and empirically evaluates a set of state-of-the-art post-pruning techniques on the simplification task. Second, it investigates the trade-off between predictive accuracy and model interpretability. Methods. The primary research method used to conduct this study and to address the research questions is experimentation. All post-pruning techniques are implemented in Python. The Random Forest models are trained, evaluated, and validated on five selected datasets with varying characteristics. Results. There is no significant difference in predictive performance between the compared techniques and none of the studied post-pruning techniques outperforms the other on all included datasets. The experimental results also show that model interpretability is proportional to model accuracy, at least for the studied settings. That is, a positive change in model interpretability is accompanied by a negative change in model accuracy. Conclusions. It is possible to reduce the size of a complex Random Forest model while retaining or improving the predictive accuracy. Moreover, the suitability of a particular post-pruning technique depends on the application area and the amount of training data available. Significantly simplified models may be less accurate than the original model but tend to be perceived as more comprehensible.
Sammanfattning Kontext. Ensemble metoder fortsätter att få mer uppmärksamhet inom maskininlärning. Då maskininlärningstekniker som genererar en enskild klassificerare eller prediktor har visat tecken på begränsad kapacitet i vissa sammanhang, har ensemble metoder vuxit fram som alternativa metoder för att åstadkomma bättre prediktiva prestanda. En av de mest intressanta och effektiva ensemble algoritmerna som har introducerats under de senaste åren är Random Forests. För att säkerställa att Random Forests uppnår en hög prediktiv noggrannhet behöver oftast ett stort antal träd användas. Resultatet av att använda ett större antal träd för att öka den prediktiva noggrannheten är en komplex modell som kan vara svår att tolka eller analysera. Problemet med det stora antalet träd ställer dessutom högre krav på såväl lagringsutrymmet som datorkraften. Syfte. Denna uppsats utforskar möjligheten att automatiskt förenkla modeller som är genererade av Random Forests i syfte att reducera storleken på modellen, öka dess tolkningsbarhet, samt bevara eller förbättra den prediktiva noggrannheten. Syftet med denna uppsats är tvåfaldigt. Vi kommer först att jämföra och empiriskt utvärdera olika beskärningstekniker. Den andra delen av uppsatsen undersöker sambandet mellan den prediktiva noggrannheten och modellens tolkningsbarhet. Metod. Den primära forskningsmetoden som har använts för att genomföra den studien är experiment. Alla beskärningstekniker är implementerade i Python. För att träna, utvärdera, samt validera de olika modellerna, har fem olika datamängder använts. Resultat. Det finns inte någon signifikant skillnad i det prediktiva prestanda mellan de jämförda teknikerna och ingen av de undersökta beskärningsteknikerna är överlägsen på alla plan. Resultat från experimenten har också visat att sambandet mellan tolkningsbarhet och noggrannhet är proportionellt, i alla fall för de studerade konfigurationerna. Det vill säga, en positiv förändring i modellens tolkningsbarhet åtföljs av en negativ förändring i modellens noggrannhet. Slutsats. Det är möjligt att reducera storleken på en komplex Random Forests modell samt bibehålla eller förbättra den prediktiva noggrannheten. Dessutom beror valet av beskärningstekniken på användningsområdet och mängden träningsdata tillgänglig. Slutligen kan modeller som är signifikant förenklade vara mindre noggranna men å andra sidan tenderar de att uppfattas som mer förståeliga.

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Fawagreh, Khaled. „On pruning and feature engineering in Random Forests“. Thesis, Robert Gordon University, 2016. http://hdl.handle.net/10059/2113.

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Random Forest (RF) is an ensemble classification technique that was developed by Leo Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for optimizing RF further by enhancing and improving its performance accuracy. This explains why there have been many extensions of RF where each extension employed a variety of techniques and strategies to improve certain aspect(s) of RF. The main focus of this dissertation is to develop new extensions of RF using new optimization techniques that, to the best of our knowledge, have never been used before to optimize RF. These techniques are clustering, the local outlier factor, diversified weighted subspaces, and replicator dynamics. Applying these techniques on RF produced four extensions which we have termed CLUB-DRF, LOFB-DRF, DSB-RF, and RDB-DR respectively. Experimental studies on 15 real datasets showed favorable results, demonstrating the potential of the proposed methods. Performance-wise, CLUB-DRF is ranked first in terms of accuracy and classifcation speed making it ideal for real-time applications, and for machines/devices with limited memory and processing power.

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Cherfaoui, Farah. „Echantillonnage pour l'accélération des méthodes à noyaux et sélection gloutonne pour les représentations parcimonieuses“. Electronic Thesis or Diss., Aix-Marseille, 2022. http://www.theses.fr/2022AIXM0256.

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Les contributions de cette thèse se divisent en deux parties. Une première partie dédiée à l’accélération des méthodes à noyaux et une seconde à l'optimisation sous contrainte de parcimonie. Les méthodes à noyaux sont largement connues et utilisées en apprentissage automatique. Toutefois, la complexité de leur mise en œuvre est élevée et elles deviennent inutilisables lorsque le nombre de données est grand. Nous proposons dans un premier temps une approximation des Ridge Leverage Scores. Nous utilisons ensuite ces scores pour définir une distribution de probabilité pour le processus d'échantillonnage de la méthode de Nyström afin d’accélérer les méthodes à noyaux. Nous proposons dans un second temps un nouveau framework basé sur les noyaux, permettant de représenter et de comparer les distributions de probabilités discrètes. Nous exploitons ensuite le lien entre notre framework et la Maximum Mean Discrepancy pour proposer une approximation précise et peu coûteuse de cette dernière. La deuxième partie de cette thèse est consacrée à l’optimisation avec contrainte de parcimonie pour l’optimisation de signaux et l’élagage de forêts aléatoires. Tout d’abord, nous prouvons sous certaines conditions sur la cohérence du dictionnaire, les propriétés de reconstruction et de convergence de l’algorithme Frank-Wolfe. Ensuite, nous utilisons l'algorithme OMP pour réduire la taille de forêts aléatoires et ainsi réduire la taille nécessaire pour son stockage. La forêt élaguée est constituée d’un sous-ensemble d’arbres de la forêt initiale sélectionnés et pondérés par OMP de manière à minimiser son erreur empirique de prédiction
The contributions of this thesis are divided into two parts. The first part is dedicated to the acceleration of kernel methods and the second to optimization under sparsity constraints. Kernel methods are widely known and used in machine learning. However, the complexity of their implementation is high and they become unusable when the number of data is large. We first propose an approximation of Ridge leverage scores. We then use these scores to define a probability distribution for the sampling process of the Nyström method in order to speed up the kernel methods. We then propose a new kernel-based framework for representing and comparing discrete probability distributions. We then exploit the link between our framework and the maximum mean discrepancy to propose an accurate and fast approximation of the latter. The second part of this thesis is devoted to optimization with sparsity constraint for signal optimization and random forest pruning. First, we prove under certain conditions on the coherence of the dictionary, the reconstruction and convergence properties of the Frank-Wolfe algorithm. Then, we use the OMP algorithm to reduce the size of random forests and thus reduce the size needed for its storage. The pruned forest consists of a subset of trees from the initial forest selected and weighted by OMP in order to minimize its empirical prediction error

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Buchteile zum Thema "Pruning random forest"

Dheenadayalan, Kumar, G. Srinivasaraghavan und V. N. Muralidhara. „Pruning a Random Forest by Learning a Learning Algorithm“. In Machine Learning and Data Mining in Pattern Recognition, 516–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41920-6_41.

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Li, Zhaozhao, Lide Wang, Ping Shen, Hui Song und Xiaomin Du. „Fault Diagnosis of MVB Based on Random Forest and Ensemble Pruning“. In Lecture Notes in Electrical Engineering, 91–100. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2866-8_9.

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Taleb Zouggar, Souad, und Abdelkader Adla. „Measures of Random Forest Pruning: Comparative Study and Experiment on Diabetic Monitoring“. In Advances in Intelligent Systems and Computing, 263–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36664-3_30.

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Kiran, B. Ravi, und Jean Serra. „Cost-Complexity Pruning of Random Forests“. In Lecture Notes in Computer Science, 222–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57240-6_18.

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Fawagreh, Khaled, Mohamed Medhat Gaber und Eyad Elyan. „CLUB-DRF: A Clustering Approach to Extreme Pruning of Random Forests“. In Research and Development in Intelligent Systems XXXII, 59–73. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25032-8_4.

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Fawagreh, Khaled, Mohamed Medhat Gaber und Eyad Elyan. „An Outlier Ranking Tree Selection Approach to Extreme Pruning of Random Forests“. In Engineering Applications of Neural Networks, 267–82. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44188-7_20.

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Ahmad, Mahmood, Xiaowei Tang und Feezan Ahmad. „Evaluation of Liquefaction-Induced Settlement Using Random Forest and REP Tree Models: Taking Pohang Earthquake as a Case of Illustration“. In Natural Hazards - Impacts, Adjustments and Resilience. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.94274.

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A liquefaction-induced settlement assessment is considered one of the major challenges in geotechnical earthquake engineering. This paper presents random forest (RF) and reduced error pruning tree (REP Tree) models for predicting settlement caused by liquefaction. Standard penetration test (SPT) data were obtained for five separate borehole sites near the Pohang Earthquake epicenter. The data used in this study comprise of four features, namely depth, unit weight, corrected SPT blow count and cyclic stress ratio. The available data is divided into two parts: training set (80%) and test set (20%). The output of the RF and REP Tree models is evaluated using statistical parameters including coefficient of correlation (r), mean absolute error (MAE), and root mean squared error (RMSE). The applications for the aforementioned approach for predicting the liquefaction-induced settlement are compared and discussed. The analysis of statistical metrics for the evaluating liquefaction-induced settlement dataset demonstrates that the RF achieved comparatively better and reliable results.

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Heutte, Laurent, Caroline Petitjean und Chesner Désir. „PRUNING TREES IN RANDOM FORESTS FOR MINIMIZING NON DETECTION IN MEDICAL IMAGING“. In Handbook of Pattern Recognition and Computer Vision, 89–107. WORLD SCIENTIFIC, 2015. http://dx.doi.org/10.1142/9789814656535_0005.

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Konferenzberichte zum Thema "Pruning random forest"

Rose, Minu, und Hani Ragab Hassen. „A Survey of Random Forest Pruning Techniques“. In 9th International Conference on Computer Science, Engineering and Applications. Aircc publishing Corporation, 2019. http://dx.doi.org/10.5121/csit.2019.91808.

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Kulkarni, Vrushali Y., und Pradeep K. Sinha. „Pruning of Random Forest classifiers: A survey and future directions“. In 2012 International Conference on Data Science & Engineering (ICDSE). IEEE, 2012. http://dx.doi.org/10.1109/icdse.2012.6282329.

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Liu, Xin, Qifeng Zhou und Fan Yang. „Leaf node-level ensemble pruning approaches based on node-sample correlation for random forest“. In IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2017. http://dx.doi.org/10.1109/iecon.2017.8217016.

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Liang, Yu-Pei, Yung-Han Hsu, Tseng-Yi Chen, Shuo-Han Chen, Hsin-Wen Wei, Tsan-sheng Hsu und Wei-Kuan Shih. „Eco-feller: Minimizing the Energy Consumption of Random Forest Algorithm by an Eco-pruning Strategy over MLC NVRAM“. In 2021 58th ACM/IEEE Design Automation Conference (DAC). IEEE, 2021. http://dx.doi.org/10.1109/dac18074.2021.9586164.

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Al-Khudafi, Abbas M., Hamzah A. Al-Sharifi, Ghareb M. Hamada, Mohamed A. Bamaga, Abdulrahman A. Kadi und A. A. Al-Gathe. „Evaluation of Different Tree-Based Machine Learning Approaches for Formation Lithology Classification“. In International Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/igs-2023-0026.

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Abstract This study aims to assess the effectiveness of several decision tree techniques for identifying formation lithology. 20966 data points from 4 wells were used to create the study's data. Lithology is determined using seven log parameters. The seven log parameters are the density log, neutron log, sonic log, gamma ray log, deep latero log, shallow latero log, and resistivity log. Different decision tree-based algorithms for classification approaches were applied. six typical machine learning models, namely the, Random Forest. Random trees, J48, reduced-error pruning decision trees, logistic model trees, HoeffdingTree were evaluated for formation lithology identification using well logging data. The obtained results shows that the random forest model, out of the proposed decision tree models, performed best at lithology identification, with precession, recall, and F-score values of 0.913, 0.914, and 0.913, respectively. Random trees Random trees came next. With average precision, recall, and F1-score of 0.837, 0.84, and 0.837, respectively, the J48 model came in third place. The HoeffdingTree classification model, however, showed the worst performance. We conclude that boosting strategies enhance the performance of tree-based models. Evaluation of prediction capability of models is also carried out using different datasets.

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Al-Sharifi, H. A., A. M. Alkhudafi, A. A. Al-Gathe, S. O. Baarimah, Wahbi Al-Ameri und A. T. Alyazidi. „Prediction of Two-Phase Flow Regimes in Vertical Pipes Using Tree-Based Ensemble Models“. In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-24084-ms.

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Abstract The multi-phase fluid transfer pattern in vertical flow through pipelines is a significant parameter to be predetermined for predicting the pressure gradient, liquid holdup, and other flow properties. In the present study, the prediction of two-phase flow patterns in vertical pipes using ensemble machine-learning classification models is presented. For this purpose, ensemble machine learning techniques including boosting, bagging, and random forest have been applied. A decision tree-based classifier is proposed, such as Random trees (RT), J48, reduced-error pruning decision trees (REPT), logistic model trees (LMT), and decision trees with naive Bayes (NBT), to predict flow regimes. Datasets consisting of more than 2250 data points were used to develop the ensemble models. The importance of attributes for different models was investigated based on a dataset consisting of 1088 data points. Feature selection was performed by applying six different optimization methods. For this task, training, and cross-validation were used. To check the performance of the classifier, a learning curve is used to determine the optimal number of training data points to use. The performance of the algorithm is evaluated based on the metrics of classification accuracy, confusion matrix, precision, recall, F1-score, and the PRC area. The boosting approach and random forest classifiers have higher prediction accuracy compared with the other ensemble methods. AdaBoost, LogitBoost, and MultiBoosting algorithms were applied as boosting approaches. Multiposting has a better performance compared with the other two techniques. The random forests provided a high level of performance. Its average precision, recall, and F1 scores are 0.957, 0.958, and 0.949, respectively. It is concluded that comparing the results of single classifiers, the ensemble algorithm performed better than the single model. As such, the accuracy rate of the prediction of flow regimes can be increased to 96%. This study presents a robust and improved technique as an alternative method for the prediction of two-phase flow regimes in vertical flow with high accuracy, low effort, and lower costs. The developed models provide satisfactory and adequate results under different conditions.

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