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Academic literature on the topic 'Tree Ensemble'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Tree Ensemble"

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Alazba, Amal, and Hamoud Aljamaan. "Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles." Applied Sciences 12, no. 9 (April 30, 2022): 4577. http://dx.doi.org/10.3390/app12094577.

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Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers. However, most of the previous work utilized ensemble models in the context of software defect prediction with the default hyperparameter values, which are considered suboptimal. In this paper, we investigate the applicability of a stacking ensemble built with fine-tuned tree-based ensembles for defect prediction. We used grid search to optimize the hyperparameters of seven tree-based ensembles: random forest, extra trees, AdaBoost, gradient boosting, histogram-based gradient boosting, XGBoost and CatBoost. Then, a stacking ensemble was built utilizing the fine-tuned tree-based ensembles. The ensembles were evaluated using 21 publicly available defect datasets. Empirical results showed large impacts of hyperparameter optimization on extra trees and random forest ensembles. Moreover, our results demonstrated the superiority of the stacking ensemble over all fine-tuned tree-based ensembles.
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WINDEATT, T., and G. ARDESHIR. "DECISION TREE SIMPLIFICATION FOR CLASSIFIER ENSEMBLES." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 05 (August 2004): 749–76. http://dx.doi.org/10.1142/s021800140400340x.

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The goal of designing an ensemble of simple classifiers is to improve the accuracy of a recognition system. However, the performance of ensemble methods is problem-dependent and the classifier learning algorithm has an important influence on ensemble performance. In particular, base classifiers that are too complex may result in overfitting. In this paper, the performance of Bagging, Boosting and Error-Correcting Output Code (ECOC) is compared for five decision tree pruning methods. A description is given for each of the pruning methods and the ensemble techniques. AdaBoost.OC which is a combination of Boosting and ECOC is compared with the pseudo-loss based version of Boosting, AdaBoost.M2 and the influence of pruning on the performance of the ensembles is studied. Motivated by the result that both pruned and unpruned ensembles made by AdaBoost.OC give similar accuracy, pruned ensembles are compared with ensembles of Decision Stumps. This leads to the hypothesis that ensembles of simple classifiers may give better performance for some problems. Using the application of face recognition, it is shown that an AdaBoost.OC ensemble of Decision Stumps outperforms an ensemble of pruned C4.5 trees for face identification, but is inferior for face verification. The implication is that in some real-world tasks to achieve best accuracy of an ensemble, it may be necessary to select base classifier complexity.
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Pahno, Steve, Jidong J. Yang, and S. Sonny Kim. "Use of Machine Learning Algorithms to Predict Subgrade Resilient Modulus." Infrastructures 6, no. 6 (May 21, 2021): 78. http://dx.doi.org/10.3390/infrastructures6060078.

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Modern machine learning methods, such as tree ensembles, have recently become extremely popular due to their versatility and scalability in handling heterogeneous data and have been successfully applied across a wide range of domains. In this study, two widely applied tree ensemble methods, i.e., random forest (parallel ensemble) and gradient boosting (sequential ensemble), were investigated to predict resilient modulus, using routinely collected soil properties. Laboratory test data on sandy soils from nine borrow pits in Georgia were used for model training and testing. For comparison purposes, the two tree ensemble methods were evaluated against a regression tree model and a multiple linear regression model, demonstrating their superior performance. The results revealed that a single tree model generally suffers from high variance, while providing a similar performance to the traditional multiple linear regression model. By leveraging a collection of trees, both tree ensemble methods, Random Forest and eXtreme Gradient Boosting, significantly reduced variance and improved prediction accuracy, with the eXtreme Gradient Boosting being the best model, with an R2 of 0.95 on the test dataset.
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PETERSON, ADAM H., and TONY R. MARTINEZ. "REDUCING DECISION TREE ENSEMBLE SIZE USING PARALLEL DECISION DAGS." International Journal on Artificial Intelligence Tools 18, no. 04 (August 2009): 613–20. http://dx.doi.org/10.1142/s0218213009000305.

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This research presents a new learning model, the Parallel Decision DAG (PDDAG), and shows how to use it to represent an ensemble of decision trees while using significantly less storage. Ensembles such as Bagging and Boosting have a high probability of encoding redundant data structures, and PDDAGs provide a way to remove this redundancy in decision tree based ensembles. When trained by encoding an ensemble, the new model behaves similar to the original ensemble, and can be made to perform identically to it. The reduced storage requirements allow an ensemble approach to be used in cases where storage requirements would normally be exceeded, and the smaller model can potentially execute faster by reducing redundant computation.
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Jiang, Xiangkui, Chang-an Wu, and Huaping Guo. "Forest Pruning Based on Branch Importance." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3162571.

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A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be achieved when a branch is pruned. Our experiments show that the proposed method can significantly reduce ensemble size and improve ensemble accuracy, no matter whether ensembles are constructed by a certain algorithm such as bagging or obtained by an ensemble selection algorithm, no matter whether each decision tree is pruned or unpruned.
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Kułaga, Rafał, and Marek Gorgoń. "FPGA Implementation of Decision Trees and Tree Ensembles for Character Recognition in Vivado Hls." Image Processing & Communications 19, no. 2-3 (September 1, 2014): 71–82. http://dx.doi.org/10.1515/ipc-2015-0012.

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Abstract Decision trees and decision tree ensembles are popular machine learning methods, used for classification and regression. In this paper, an FPGA implementation of decision trees and tree ensembles for letter and digit recognition in Vivado High-Level Synthesis is presented. Two publicly available datasets were used at both training and testing stages. Different optimizations for tree code and tree node layout in memory are considered. Classification accuracy, throughput and resource usage for different training algorithms, tree depths and ensemble sizes are discussed. The correctness of the module’s operation was verified using C/RTL cosimulation and on a Zynq-7000 SoC device, using Xillybus IP core for data transfer between the processing system and the programmable logic.
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Ranzato, Francesco, and Marco Zanella. "Abstract Interpretation of Decision Tree Ensemble Classifiers." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5478–86. http://dx.doi.org/10.1609/aaai.v34i04.5998.

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We study the problem of formally and automatically verifying robustness properties of decision tree ensemble classifiers such as random forests and gradient boosted decision tree models. A recent stream of works showed how abstract interpretation, which is ubiquitously used in static program analysis, can be successfully deployed to formally verify (deep) neural networks. In this work we push forward this line of research by designing a general and principled abstract interpretation-based framework for the formal verification of robustness and stability properties of decision tree ensemble models. Our abstract interpretation-based method may induce complete robustness checks of standard adversarial perturbations and output concrete adversarial attacks. We implemented our abstract verification technique in a tool called silva, which leverages an abstract domain of not necessarily closed real hyperrectangles and is instantiated to verify random forests and gradient boosted decision trees. Our experimental evaluation on the MNIST dataset shows that silva provides a precise and efficient tool which advances the current state of the art in tree ensembles verification.
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Louk, Maya Hilda Lestari, and Bayu Adhi Tama. "Tree-Based Classifier Ensembles for PE Malware Analysis: A Performance Revisit." Algorithms 15, no. 9 (September 17, 2022): 332. http://dx.doi.org/10.3390/a15090332.

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Given their escalating number and variety, combating malware is becoming increasingly strenuous. Machine learning techniques are often used in the literature to automatically discover the models and patterns behind such challenges and create solutions that can maintain the rapid pace at which malware evolves. This article compares various tree-based ensemble learning methods that have been proposed in the analysis of PE malware. A tree-based ensemble is an unconventional learning paradigm that constructs and combines a collection of base learners (e.g., decision trees), as opposed to the conventional learning paradigm, which aims to construct individual learners from training data. Several tree-based ensemble techniques, such as random forest, XGBoost, CatBoost, GBM, and LightGBM, are taken into consideration and are appraised using different performance measures, such as accuracy, MCC, precision, recall, AUC, and F1. In addition, the experiment includes many public datasets, such as BODMAS, Kaggle, and CIC-MalMem-2022, to demonstrate the generalizability of the classifiers in a variety of contexts. Based on the test findings, all tree-based ensembles performed well, and performance differences between algorithms are not statistically significant, particularly when their respective hyperparameters are appropriately configured. The proposed tree-based ensemble techniques also outperformed other, similar PE malware detectors that have been published in recent years.
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Buschjäger, Sebastian, Sibylle Hess, and Katharina J. Morik. "Shrub Ensembles for Online Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6123–31. http://dx.doi.org/10.1609/aaai.v36i6.20560.

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Online learning algorithms have become a ubiquitous tool in the machine learning toolbox and are frequently used in small, resource-constraint environments. Among the most successful online learning methods are Decision Tree (DT) ensembles. DT ensembles provide excellent performance while adapting to changes in the data, but they are not resource efficient. Incremental tree learners keep adding new nodes to the tree but never remove old ones increasing the memory consumption over time. Gradient-based tree learning, on the other hand, requires the computation of gradients over the entire tree which is costly for even moderately sized trees. In this paper, we propose a novel memory-efficient online classification ensemble called shrub ensembles for resource-constraint systems. Our algorithm trains small to medium-sized decision trees on small windows and uses stochastic proximal gradient descent to learn the ensemble weights of these `shrubs'. We provide a theoretical analysis of our algorithm and include an extensive discussion on the behavior of our approach in the online setting. In a series of 2~959 experiments on 12 different datasets, we compare our method against 8 state-of-the-art methods. Our Shrub Ensembles retain an excellent performance even when only little memory is available. We show that SE offers a better accuracy-memory trade-off in 7 of 12 cases, while having a statistically significant better performance than most other methods. Our implementation is available under https://github.com/sbuschjaeger/se-online .
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Franzese, Giulio, and Monica Visintin. "Probabilistic Ensemble of Deep Information Networks." Entropy 22, no. 1 (January 14, 2020): 100. http://dx.doi.org/10.3390/e22010100.

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We describe a classifier made of an ensemble of decision trees, designed using information theory concepts. In contrast to algorithms C4.5 or ID3, the tree is built from the leaves instead of the root. Each tree is made of nodes trained independently of the others, to minimize a local cost function (information bottleneck). The trained tree outputs the estimated probabilities of the classes given the input datum, and the outputs of many trees are combined to decide the class. We show that the system is able to provide results comparable to those of the tree classifier in terms of accuracy, while it shows many advantages in terms of modularity, reduced complexity, and memory requirements.
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Dissertations / Theses on the topic "Tree Ensemble"

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Elias, Joran. "Randomness In Tree Ensemble Methods." The University of Montana, 2009. http://etd.lib.umt.edu/theses/available/etd-10092009-110301/.

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Tree ensembles have proven to be a popular and powerful tool for predictive modeling tasks. The theory behind several of these methods (e.g. boosting) has received considerable attention. However, other tree ensemble techniques (e.g. bagging, random forests) have attracted limited theoretical treatment. Specifically, it has remained somewhat unclear as to why the simple act of randomizing the tree growing algorithm should lead to such dramatic improvements in performance. It has been suggested that a specific type of tree ensemble acts by forming a locally adaptive distance metric [Lin and Jeon, 2006]. We generalize this claim to include all tree ensembles methods and argue that this insight can help to explain the exceptional performance of tree ensemble methods. Finally, we illustrate the use of tree ensemble methods for an ecological niche modeling example involving the presence of malaria vectors in Africa.
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Zhang, Yi. "Strategies for Combining Tree-Based Ensemble Models." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1021.

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Ensemble models have proved effective in a variety of classification tasks. These models combine the predictions of several base models to achieve higher out-of-sample classification accuracy than the base models. Base models are typically trained using different subsets of training examples and input features. Ensemble classifiers are particularly effective when their constituent base models are diverse in terms of their prediction accuracy in different regions of the feature space. This dissertation investigated methods for combining ensemble models, treating them as base models. The goal is to develop a strategy for combining ensemble classifiers that results in higher classification accuracy than the constituent ensemble models. Three of the best performing tree-based ensemble methods – random forest, extremely randomized tree, and eXtreme gradient boosting model – were used to generate a set of base models. Outputs from classifiers generated by these methods were then combined to create an ensemble classifier. This dissertation systematically investigated methods for (1) selecting a set of diverse base models, and (2) combining the selected base models. The methods were evaluated using public domain data sets which have been extensively used for benchmarking classification models. The research established that applying random forest as the final ensemble method to integrate selected base models and factor scores of multiple correspondence analysis turned out to be the best ensemble approach.
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De, Giorgi Marcello. "Tree ensemble methods for Predictive Maintenance: a case study." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22282/.

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Nel lavoro descritto in questa tesi sono stati creati modelli per la manutenzione predittiva di macchine utensili in ambito industriale; in particolare, i modelli realizzati sono stati addestrati sfruttando degli ensemble tree methods con le finalità di: predire il verificarsi di un guasto in macchina con un anticipo tale da permettere l'organizzazione delle squadre di manutenzione; predire la necessità della sostituzione anticipata dell'utensile utilizzato dalla macchina, per mantenere alti gli standard di qualità. Dopo aver dato uno sfondo al contesto industriale in esame, la tesi illustra i processi seguiti per la creazione e l'aggregazione di un dataset, e l'introduzione di informazioni relative agli eventi in macchina. Analizzato il comportamento di alcune variabili durante la lavorazione ed effettuata una distinzione tra cicli di lavorazione validi e non validi, si procede introducendo gli ensemble tree methods e il motivo della scelta di questa classe di algoritmi. Nel dettaglio, vengono presentati due possibili candidati al problema trattato: Random Forest ed XGBoost; dopo averne descritto il funzionamento, vengono presentati i risultati ottenuti dai modelli proponendo, per stimarne l'efficacia, un funzione di costo atteso come alternativa all'accuracy score. I risultati dei modelli allenati con i due algoritmi proposti vengono infine confrontati.
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Alcaçoas, Dellainey. "Anomaly detection in ring rolling process : Using Tree Ensemble Methods." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18400.

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Anomaly detection has been studied for many years and has been implemented successfully in many domains. There are various approaches one could adopt to achieve this goal. The core idea behind these is to build a model that is trained in detecting patterns of anomalies. For this thesis, the objective was to detect anomalies and identify the causes for the same given the data about the process in a manufacturing setup. The scenario chosen was of a ring rolling process followed at Ovako steel company in Hofors, Sweden. An approach involving tree ensemble method coupled with manual feature engineering of multivariate time series was adopted. Through the various experiments performed, it was found that the approach was successful in detecting anomalies with an accuracy varying between 79% to 82%. To identify the causes of anomalies, feature importance using Shapley additive explanation method was implemented. Doing so, identified one feature that was very prominent and could be the potential cause for anomaly. In this report, the scope for improvement and future work has also been suggested.
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Gupta, Suraj. "Metagenomic Data Analysis Using Extremely Randomized Tree Algorithm." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/96025.

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Many antibiotic resistance genes (ARGs) conferring resistance to a broad range of antibiotics have often been detected in aquatic environments such as untreated and treated wastewater, river and surface water. ARG proliferation in the aquatic environment could depend upon various factors such as geospatial variations, the type of aquatic body, and the type of wastewater (untreated or treated) discharged into these aquatic environments. Likewise, the strong interconnectivity of aquatic systems may accelerate the spread of ARGs through them. Hence a comparative and a holistic study of different aquatic environments is required to appropriately comprehend the problem of antibiotic resistance. Many studies approach this issue using molecular techniques such as metagenomic sequencing and metagenomic data analysis. Such analyses compare the broad spectrum of ARGs in water and wastewater samples, but these studies use comparisons which are limited to similarity/dissimilarity analyses. However, in such analyses, the discriminatory ARGs (associated ARGs driving such similarity/ dissimilarity measures) may not be identified. Consequentially, the reason which drives the dissimilarities among the samples would not be identified and the reason for antibiotic resistance proliferation may not be clearly understood. In this study, an effective methodology, using Extremely Randomized Trees (ET) Algorithm, was formulated and demonstrated to capture such ARG variations and identify discriminatory ARGs among environmentally derived metagenomes. In this study, data were grouped by: geographic location (to understand the spread of ARGs globally), untreated vs. treated wastewater (to see the effectiveness of WWTPs in removing ARGs), and different aquatic habitats (to understand the impact and spread within aquatic habitats). It was observed that there were certain ARGs which were specific to wastewater samples from certain locations suggesting that site-specific factors can have a certain effect in shaping ARG profiles. Comparing untreated and treated wastewater samples from different WWTPs revealed that biological treatments have a definite impact on shaping the ARG profile. While there were several ARGs which got removed after the treatment, there were some ARGs which showed an increase in relative abundance irrespective of location and treatment plant specific variables. On comparing different aquatic environments, the algorithm identified ARGs which were specific to certain environments. The algorithm captured certain ARGs which were specific to hospital discharges when compared with other aquatic environments. It was determined that the proposed method was efficient in identifying the discriminatory ARGs which could classify the samples according to their groups. Further, it was also effective in capturing low-level variations which generally get over-shadowed in the analysis due to highly abundant genes. The results of this study suggest that the proposed method is an effective method for comprehensive analyses and can provide valuable information to better understand antibiotic resistance.
MS
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Assareh, Amin. "OPTIMIZING DECISION TREE ENSEMBLES FOR GENE-GENE INTERACTION DETECTION." Kent State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1353971575.

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Chakraborty, Debaditya. "Detection of Faults in HVAC Systems using Tree-based Ensemble Models and Dynamic Thresholds." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543582336141076.

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Bogdan, Vukobratović. "Hardware Acceleration of Nonincremental Algorithms for the Induction of Decision Trees and Decision Tree Ensembles." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=102520&source=NDLTD&language=en.

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The thesis proposes novel full decision tree and decision tree ensembleinduction algorithms EFTI and EEFTI, and various possibilities for theirimplementations are explored. The experiments show that the proposed EFTIalgorithm is able to infer much smaller DTs on average, without thesignificant loss in accuracy, when compared to the top-down incremental DTinducers. On the other hand, when compared to other full tree inductionalgorithms, it was able to produce more accurate DTs, with similar sizes, inshorter times. Also, the hardware architectures for acceleration of thesealgorithms (EFTIP and EEFTIP) are proposed and it is shown in experimentsthat they can offer substantial speedups.
У овоj дисертациjи, представљени су нови алгоритми EFTI и EEFTI заформирање стабала одлуке и њихових ансамбала неинкременталномметодом, као и разне могућности за њихову имплементациjу.Експерименти показуjу да jе предложени EFTI алгоритам у могућностида произведе драстично мања стабла без губитка тачности у односу напостојеће top-down инкременталне алгоритме, а стабла знатно већетачности у односу на постојеће неинкременталне алгоритме. Такође супредложене хардверске архитектуре за акцелерацију ових алгоритама(EFTIP и EEFTIP) и показано је да је уз помоћ ових архитектура могућеостварити знатна убрзања.
U ovoj disertaciji, predstavljeni su novi algoritmi EFTI i EEFTI zaformiranje stabala odluke i njihovih ansambala neinkrementalnommetodom, kao i razne mogućnosti za njihovu implementaciju.Eksperimenti pokazuju da je predloženi EFTI algoritam u mogućnostida proizvede drastično manja stabla bez gubitka tačnosti u odnosu napostojeće top-down inkrementalne algoritme, a stabla znatno većetačnosti u odnosu na postojeće neinkrementalne algoritme. Takođe supredložene hardverske arhitekture za akceleraciju ovih algoritama(EFTIP i EEFTIP) i pokazano je da je uz pomoć ovih arhitektura mogućeostvariti znatna ubrzanja.
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Whitley, Michael Aaron. "Using statistical learning to predict survival of passengers on the RMS Titanic." Kansas State University, 2015. http://hdl.handle.net/2097/20541.

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Master of Science
Statistics
Christopher Vahl
When exploring data, predictive analytics techniques have proven to be effective. In this report, the efficiency of several predictive analytics methods are explored. During the time of this study, Kaggle.com, a data science competition website, had the predictive modeling competition, "Titanic: Machine Learning from Disaster" available. This competition posed a classification problem to build a predictive model to predict the survival of passengers on the RMS Titanic. The focus of our approach was on applying a traditional classification and regression tree algorithm. The algorithm is greedy and can over fit the training data, which consequently can yield non-optimal prediction accuracy. In efforts to correct such issues with using the classification and regression tree algorithm, we have implemented cost complexity pruning and ensemble methods such as bagging and random forests. However, no improvement was observed here which may be an artifact associated with the Titanic data and may not be representative of those methods’ performances. The decision trees and prediction accuracy of each method are presented and compared. Results indicate that the predictors sex/title, fare price, age, and passenger class are the most important variables in predicting survival of the passengers.
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Velka, Elina. "Loss Given Default Estimation with Machine Learning Ensemble Methods." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279846.

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This thesis evaluates the performance of three machine learning methods in prediction of the Loss Given Default (LGD). LGD can be seen as the opposite of the recovery rate, i.e. the ratio of an outstanding loan that the loan issuer would not be able to recover in case the customer would default. The methods investigated are decision trees, random forest and boosted methods. All of the methods investigated performed well in predicting the cases were the loan is not recovered, LGD = 1 (100%), or the loan is totally recovered, LGD = 0 (0% ). When the performance of the models was evaluated on a dataset where the observations with LGD = 1 were removed, a significant decrease in performance was observed. The random forest model built on an unbalanced training dataset showed better performance on the test dataset that included values LGD = 1 and the random forest model built on a balanced training dataset performed better on the test set where the observations of LGD = 1 were removed. Boosted models evaluated in this study showed less accurate predictions than other methods used. Overall, the performance of random forest models showed slightly better results than the performance of decision tree models, although the computational time (the cost) was considerably longer when running the random forest models. Therefore decision tree models would be suggested for prediction of the Loss Given Default.
Denna uppsats undersöker och jämför tre maskininlärningsmetoder som estimerar förlust vid fallissemang (Loss Given Default, LGD). LGD kan ses som motsatsen till återhämtningsgrad, dvs. andelen av det utstående lånet som långivaren inte skulle återfå ifall kunden skulle fallera. Maskininlärningsmetoder som undersöks i detta arbete är decision trees, random forest och boosted metoder. Alla metoder fungerade väl vid estimering av lån som antingen inte återbetalas, dvs. LGD = 1 (100%), eller av lån som betalas i sin helhet, LGD = 0 (0%). En tydlig minskning i modellernas träffsäkerhet påvisades när modellerna kördes med ett dataset där observationer med LGD = 1 var borttagna. Random forest modeller byggda på ett obalanserat träningsdataset presterade bättre än de övriga modellerna på testset som inkluderade observationer där LGD = 1. Då observationer med LGD = 1 var borttagna visade det sig att random forest modeller byggda på ett balanserat träningsdataset presterade bättre än de övriga modellerna. Boosted modeller visade den svagaste träffsäkerheten av de tre metoderna som blev undersökta i denna studie. Totalt sett visade studien att random forest modeller byggda på ett obalanserat träningsdataset presterade en aning bättre än decision tree modeller, men beräkningstiden (kostnaden) var betydligt längre när random forest modeller kördes. Därför skulle decision tree modeller föredras vid estimering av förlust vid fallissemang.
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Books on the topic "Tree Ensemble"

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Kozak, Jan. Decision Tree and Ensemble Learning Based on Ant Colony Optimization. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-93752-6.

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Kozak, Jan. Decision Tree and Ensemble Learning Based on Ant Colony Optimization. Springer, 2018.

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Kozak, Jan. Decision Tree and Ensemble Learning Based on Ant Colony Optimization. Springer, 2019.

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Randomized Ensemble Methods for Classification Trees. Storming Media, 2002.

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Speicher, Roland. Random banded and sparse matrices. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.23.

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This article discusses some mathematical results and conjectures about random band matrix ensembles (RBM) and sparse matrix ensembles. Spectral problems of RBM and sparse matrices can be expressed in terms of supersymmetric (SUSY) statistical mechanics that provides a dual representation for disordered quantum systems. This representation offers important insights into nonperturbative aspects of the spectrum and eigenfunctions of RBM. The article first presents the definition of RBM ensembles before considering the density of states, the behaviour of eigenvectors, and eigenvalue statistics for RBM and sparse random matrices. In particular, it highlights the relations with random Schrödinger (RS) and the role of the dimension of the lattice. It also describes the connection between RBM and statistical mechanics, the spectral theory of large random sparse matrices, conjectures and theorems about eigenvectors and local spacing statistics, and the RS operator on the Cayley tree or Bethe lattice.
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van Moerbeke, Pierre. Determinantal point processes. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.11.

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This article presents a list of algebraic, combinatorial, and analytic mechanisms that give rise to determinantal point processes. Determinantal point processes have been used in random matrix theory (RMT) since the early 1960s. As a separate class, determinantal processes were first used to model fermions in thermal equilibrium and the term ‘fermion’ point processes were adopted. The article first provides an overview of the generalities associated with determinantal point processes before discussing loop-free Markov chains, that is, the trajectories of the Markov chain do not pass through the same point twice almost surely. It then considers the measures given by products of determinants, namely, biorthogonal ensembles. An especially important subclass of biorthogonal ensembles consists of orthogonal polynomial ensembles. The article also describes L-ensembles, a general construction of determinantal point processes via the Fock space formalism, dimer models, uniform spanning trees, Hermitian correlation kernels, and Pfaffian point processes.
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López, César Pérez. DATA MINING and MACHINE LEARNING. PREDICTIVE TECHNIQUES : ENSEMBLE METHODS, BOOSTING, BAGGING, RANDOM FOREST, DECISION TREES and REGRESSION TREES.: Examples with MATLAB. Lulu Press, Inc., 2021.

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Pomey, Patrice. Defining a Ship: Architecture, Function, and Human Space. Edited by Ben Ford, Donny L. Hamilton, and Alexis Catsambis. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199336005.013.0001.

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This article is an introduction to the concept of maritime archaeology. In the field of archaeology, the study of a shipwreck endeavors to reconstitute the original ship. Thus, nautical archaeology belongs to the larger domain of maritime archaeology. The study of shipboard artifacts and cargo comes before a structural analysis is possible. Therefore, one must know how to anticipate the expected results in order to take into consideration the ensemble of data. A ship is an assembly of elements closely linked together, which express their true role in their relation to the whole. This article explains the conception phase. Several operations are necessary to achieve construction of a ship. The conception phase must then lead to a realization phase. The realization phase must materialize, with the help of diverse processes or methods, the construction principles chosen for the structural and shape concept of the ship.
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Bjella, Richard. The Art of Successful Programming. Edited by Frank Abrahams and Paul D. Head. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199373369.013.16.

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It seems that many concert programs are presented without enough concern for the overall flow, purpose, and direction of the choral performance itself. Often, many wonderful selections are included, but rarely do they truly work together in tandem or with enough significant diversity and color changes to warrant the audiences complete attention. Several unique models for programming at all levels are discussed. Questions are raised concerning choral programming tendencies (from Psalm choral settings to mixed meter music to Carmina Burana) and how the building of varied repertoires and unorthodox pairings can assist true success. In this age of diminishing crowds, fiscal resources, and rehearsal time, our ability to creatively weave the material to capture our singers and our audiences at the same time is extremely critical. Finally, we touch upon engaging the audience from the moment the ensemble takes the stage until the final ovation.
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Fletcher, Roland, Brendan M. Buckley, Christophe Pottier, and Shi-Yu Simon Wang. Fourteenth to Sixteenth Centuries AD. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199329199.003.0010.

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Angkor, the capital of the Khmer Empire in Southeast Asia, was the most extensive low-density agrarian-based urban complex in the world. The demise of this great city between the late 13th and the start of the 17th centuries AD has been a topic of ongoing debate, with explanations that range from the burden of excessive construction work to disease, geo-political change, and the development of new trade routes. In the 1970s Bernard-Phillipe Groslier argued for the adverse effects of land clearance and deteriorating rice yields. What can now be added to this ensemble of explanations is the role of the massive inertia of Angkor’s immense water management system, political dependence on a meticulously organized risk management system for ensuring rice production, and the impact of extreme climate anomalies from the 14th to the 16th centuries that brought intense, high-magnitude monsoons interspersed with decades-long drought. Evidence of this severe climatic instability is found in a seven-and-a-half century tree-ring record from tropical southern Vietnam. The climatic instability at the time of Angkor’s demise coincides with the abrupt transition from wetter, La Niña-like conditions over Indochina during the Medieval Warm Period to the more drought-dominated climate of the Little Ice Age, when El Niño appears to have dominated and the ITCZ migrated nearly five degrees southward. As this transition neared, Angkor was hit by the double impact of high-magnitude rains and crippling droughts, the former causing damage to water management infrastructure and the latter decreasing agricultural productivity. The Khmer state at Angkor was built on a human-engineered, artificial wetland fed by small rivers. The management of water was a massive undertaking, and the state potentially possessed the capacity to ride out drought, as it had done for the first half of the 13th century. Indeed, Angkor demonstrated just how powerful a water management system would be required and, conversely, how formidable a threat drought can be. The irony, then, is that extreme flooding destroyed Angkor’s water management capacity and removed a system that was designed to protect its population from climate anomalies.
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Book chapters on the topic "Tree Ensemble"

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Greenwell, Brandon M. "Ensemble algorithms." In Tree-Based Methods for Statistical Learning in R, 179–202. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003089032-5.

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Krętowska, Małgorzata. "Competing Risks and Survival Tree Ensemble." In Artificial Intelligence and Soft Computing, 387–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29347-4_45.

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Abou-Zleikha, Mohamed, Zheng-Hua Tan, Mads Græsbøll Christensen, and Søren Holdt Jensen. "Utilising Tree-Based Ensemble Learning for Speaker Segmentation." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 50–59. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44654-6_5.

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Yasodhara, Angeline, Azin Asgarian, Diego Huang, and Parinaz Sobhani. "On the Trustworthiness of Tree Ensemble Explainability Methods." In Lecture Notes in Computer Science, 293–308. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84060-0_19.

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Ramírez, Javier, Juan M. Górriz, Andrés Ortiz, Pablo Padilla, and Francisco J. Martínez-Murcia. "Ensemble Tree Learning Techniques for Magnetic Resonance Image Analysis." In Innovation in Medicine and Healthcare 2015, 395–404. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23024-5_36.

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Qi, Feng, Xiyu Liu, and Yinghong Ma. "A Neural Tree Network Ensemble Mode for Disease Classification." In Lecture Notes in Electrical Engineering, 1791–96. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7618-0_209.

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Kaing, Davin, and Larry Medsker. "Competitive Hybrid Ensemble Using Neural Network and Decision Tree." In Fuzzy Logic in Intelligent System Design, 147–55. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67137-6_16.

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Saha, Sriparna, Biswarup Ganguly, and Amit Konar. "Gesture Recognition from Two-Person Interactions Using Ensemble Decision Tree." In Advances in Intelligent Systems and Computing, 287–93. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3373-5_29.

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Bhati, Bhoopesh Singh, and C. S. Rai. "Ensemble Based Approach for Intrusion Detection Using Extra Tree Classifier." In Intelligent Computing in Engineering, 213–20. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2780-7_25.

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Sepiolo, Dominik, and Antoni Ligęza. "Towards Explainability of Tree-Based Ensemble Models. A Critical Overview." In New Advances in Dependability of Networks and Systems, 287–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06746-4_28.

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Conference papers on the topic "Tree Ensemble"

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Dissado, L. A., S. M. Rowland, J. C. Fillipini, J. C. Fothergill, S. V. Wolfe, and C. T. Meyer. "Individual & ensemble water tree growth." In Conference on Electrical Insulation & Dielectric Phenomena - Annual Report 1986. IEEE, 1986. http://dx.doi.org/10.1109/ceidp.1986.7726477.

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Larasati, Retno, and Hak KeungLam. "Handwritten digits recognition using ensemble neural networks and ensemble decision tree." In 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS). IEEE, 2017. http://dx.doi.org/10.1109/icon-sonics.2017.8267829.

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Ma, Shugao, Leonid Sigal, and Stan Sclaroff. "Space-time tree ensemble for action recognition." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7299137.

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Ramchandran, Maya, Prasad Patil, and Giovanni Parmigiani. "Tree-Weighting for Multi-Study Ensemble Learners." In Pacific Symposium on Biocomputing 2020. WORLD SCIENTIFIC, 2019. http://dx.doi.org/10.1142/9789811215636_0040.

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Ezeh, Dubem. "On packet classification using a decision-tree ensemble." In CoNEXT '20: The 16th International Conference on emerging Networking EXperiments and Technologies. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3426746.3434054.

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Karakatič, Sašo, and Vili Podgorelec. "Building boosted classification tree ensemble with genetic programming." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3205774.

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Bin, Guangyu, Minggang Shao, Guanghong Bin, Jiao Huang, Dingchang Zheng, and Shuicai Wu. "Detection of Atrial Fibrillation Using Decision Tree Ensemble." In 2017 Computing in Cardiology Conference. Computing in Cardiology, 2017. http://dx.doi.org/10.22489/cinc.2017.342-204.

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Wang, Bohao. "Tree Ensemble Property Verification from A Testing Perspective." In The 33rd International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc., 2021. http://dx.doi.org/10.18293/seke2021-087.

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Gulowaty, Bogdan, and Michal Wozniak. "Extracting Interpretable Decision Tree Ensemble from Random Forest." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533601.

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Kamahori, Keisuke, and Shinya Takamaeda-Yamazaki. "Accelerating Decision Tree Ensemble with Guided Branch Approximation." In HEART2022: International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3535044.3535048.

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Reports on the topic "Tree Ensemble"

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Prenger, R., B. Chen, T. Marlatt, and D. Merl. Fast MAP Search for Compact Additive Tree Ensembles (CATE). Office of Scientific and Technical Information (OSTI), March 2013. http://dx.doi.org/10.2172/1078539.

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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.
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Derbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.

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Recently the statistical characterizations of financial markets based on physics concepts and methods attract considerable attentions. The correlation matrix formalism and concept of multifractality are used to study temporal aspects of the Ukraine Stock Market evolution. Random matrix theory (RMT) is carried out using daily returns of 431 stocks extracted from database time series of prices the First Stock Trade System index (www.kinto.com) for the ten-year period 1997-2006. We find that a majority of the eigenvalues of C fall within the RMT bounds for the eigenvalues of random correlation matrices. We test the eigenvalues of C within the RMT bound for universal properties of random matrices and find good agreement with the results for the Gaussian orthogonal ensemble of random matrices—implying a large degree of randomness in the measured cross-correlation coefficients. Further, we find that the distribution of eigenvector components for the eigenvectors corresponding to the eigenvalues outside the RMT bound display systematic deviations from the RMT prediction. We analyze the components of the deviating eigenvectors and find that the largest eigenvalue corresponds to an influence common to all stocks. Our analysis of the remaining deviating eigenvectors shows distinct groups, whose identities correspond to conventionally identified business sectors. Comparison with the Mantegna minimum spanning trees method gives a satisfactory consent. The found out the pseudoeffects related to the artificial unchanging areas of price series come into question We used two possible procedures of analyzing multifractal properties of a time series. The first one uses the continuous wavelet transform and extracts scaling exponents from the wavelet transform amplitudes over all scales. The second method is the multifractal version of the detrended fluctuation analysis method (MF-DFA). The multifractality of a time series we analysed by means of the difference of values singularity stregth (or Holder exponent) ®max and ®min as a suitable way to characterise multifractality. Singularity spectrum calculated from daily returns using a sliding 250 day time window in discrete steps of 1. . . 10 days. We discovered that changes in the multifractal spectrum display distinctive pattern around significant “drawdowns”. Finally, we discuss applications to the construction of crushes precursors at the financial markets.
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