Дисертації з теми "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/.
Повний текст джерелаZhang, Yi. "Strategies for Combining Tree-Based Ensemble Models." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1021.
Повний текст джерелаDe, 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/.
Повний текст джерелаAlcaçoas, 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.
Повний текст джерелаGupta, Suraj. "Metagenomic Data Analysis Using Extremely Randomized Tree Algorithm." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/96025.
Повний текст джерелаMS
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.
Повний текст джерелаChakraborty, 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.
Повний текст джерелаBogdan, 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.
Повний текст джерелаУ 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.
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.
Повний текст джерелаStatistics
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.
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.
Повний текст джерелаDenna 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.
Alfuhaid, Abdulaziz Ataallah. "AN AGENT-BASED SYSTEMATIC ENSEMBLE APPROACH FOR AUTO AUCTION PREDICTION." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1542560217326084.
Повний текст джерелаAlesand, Elias. "Identification of Flying Drones in Mobile Networks using Machine Learning." Thesis, Linköpings universitet, Kommunikationssystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157627.
Повний текст джерелаMitchell, Andrew Computer Science & Engineering Faculty of Engineering UNSW. "An approach to boosting from positive-only data." Awarded by:University of New South Wales. Computer Science and Engineering, 2004. http://handle.unsw.edu.au/1959.4/20678.
Повний текст джерелаArdeshir, G. "Decision tree simplification for classifier ensembles." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/843022/.
Повний текст джерелаAhmad, Amir. "Data Transformation for Decision Tree Ensembles." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508528.
Повний текст джерелаKobayashi, Izumi. "Randomized ensemble methods for classification trees." Diss., Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FKobayashi.pdf.
Повний текст джерелаDissertation supervisor: Samuel E. Buttrey. Includes bibliographical references (p. 117-119). Also available online.
Sinsel, Erik W. "Ensemble learning for ranking interesting attributes." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4400.
Повний текст джерелаTitle from document title page. Document formatted into pages; contains viii, 81 p. : ill. Includes abstract. Includes bibliographical references (p. 72-74).
Pisetta, Vincent. "New Insights into Decision Trees Ensembles." Thesis, Lyon 2, 2012. http://www.theses.fr/2012LYO20018/document.
Повний текст джерелаDecision trees ensembles are among the most popular tools in machine learning. Nevertheless, their theoretical properties as well as their empirical performances are subject to strong investigation up to date. In this thesis, we propose to shed light on these methods. More precisely, after having described the current theoretical aspects of three main ensemble schemes (chapter 1), we give an analysis supporting the existence of common reasons to the success of these three principles (chapter 2). This last takes into account the two first moments of the margin as an essential ingredient to obtain strong learning abilities. Starting from this rejoinder, we propose a new ensemble algorithm called OSS (Oriented Sub-Sampling) whose steps are in perfect accordance with the point of view we introduce. The empirical performances of OSS are superior to the ones of currently popular algorithms such as Random Forests and AdaBoost. In a third chapter (chapter 3), we analyze Random Forests adopting a “kernel” point of view. This last allows us to understand and observe the underlying regularization mechanism of these kinds of methods. Adopting the kernel point of view also enables us to improve the predictive performance of Random Forests using popular post-processing techniques such as SVM and multiple kernel learning. In conjunction with random Forests, they show greatly improved performances and are able to realize a pruning of the ensemble by conserving only a small fraction of the initial base learners
Rosales, Elisa Renee. "Predicting Patient Satisfaction With Ensemble Methods." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/595.
Повний текст джерелаAhmed, Istiak. "An ensemble learning approach based on decision trees and probabilistic argumentation." Thesis, Umeå universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-175967.
Повний текст джерелаBaffoe, Nana Ama Appiaa. "Diagnostic Tools for Forecast Ensembles." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522964882574611.
Повний текст джерелаRangemo, Jesper. "Basisten bestämmer : Ett experiment i att leda en ensemble utefter tre olika metoder." Thesis, Luleå tekniska universitet, Institutionen för konst, kommunikation och lärande, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-67215.
Повний текст джерелаGangadhara, Kanthi, and Dubbaka Sai Anusha Reddy. "Comparing Compound and Ordinary Diversity measures Using Decision Trees." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20385.
Повний текст джерелаProgram: Magisterutbildning i informatik
Haris, Daniel. "Optimalizace strojového učení pro predikci KPI." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385922.
Повний текст джерелаBrugeat, Céline. "Quand l'Amérique collectionnait des cloîtres gothiques : les ensembles de Trie-sur-Baïse, Bonnefont-en-Comminges et Montréjeau." Thesis, Toulouse 2, 2016. http://www.theses.fr/2016TOU20036.
Повний текст джерелаThree cloisters attributed to the monasteries of "Trie-sur-Baise", " Bonnefont-en-Comminges" (the Cloisters, New York) and "Montréjeau" (Paradise Island, Bahamas) were purchased by American collectors and rebuilt, during the XXth century, in North America. The modern assembly of such monuments generates interest on the taste of these American amateurs, from the beginning of XXth century, for medieval European architecture. While respectively attributed to the monasteries of "Trie-sur-Baise", "Bonnefont-en-Comminges" (the Cloisters, New York) and "Montréjeau" (Paradise Island, Bahamas), the initial attribution states that the stones were from central Pyrenees monasteries, whose ruins were scattered throughout ancient times : the Hundred-year war as well as the wars of religion, the gradual desertion of religious institutions by their communities during the XVIIth and XVIIIth centuries and, at last, the alienation of their properties during the Revolution seriously damaged the integrity of monastic buildings. However, during the post-revolutionary period until the early XXth century, many discrete transactions between individuals and antique dealers further took away the stones real origin from the collective memory, especially cloisters sculptures coveted for their ornament. Identifying the cloisters provenance was the main subject of this study. The three carved marbles present various iconography ; while the "Bonnefont-en-Comminges" and "Montréjeau" ensembles both show stylized foliage ornaments, the "Trie-sur-Baise" cloister depicts original figurative scenes. Carrying out an in-depth study of these sculptures made it possible to accurately associate the cloisters to their original architectural set and production context
Johansson, Samuel, and Karol Wojtulewicz. "Machine learning algorithms in a distributed context." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148920.
Повний текст джерелаLundberg, Jacob. "Resource Efficient Representation of Machine Learning Models : investigating optimization options for decision trees in embedded systems." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162013.
Повний текст джерелаBruynooghe, Michel. "Nouveaux algorithmes en classification automatique applicables aux tres grands ensembles de donnees rencontres en traitement d'images et en reconnaissance des formes." Paris 6, 1989. http://www.theses.fr/1989PA066076.
Повний текст джерелаPobi, Shibendra. "A study of machine learning performance in the prediction of juvenile diabetes from clinical test results." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001671.
Повний текст джерелаSantos, Ant?nio de P?dua dos. "Imagin?rio radical e educa??o f?sica: trajet?ria esportiva de corredores de longa dist?ncia." Universidade Federal do Rio Grande do Norte, 2008. http://repositorio.ufrn.br:8080/jspui/handle/123456789/14170.
Повний текст джерелаCette ?tude propose une lecture du sport d endurance, prenant comme perspective th?orique l imaginaire radical et consid?rant les dimensions socio-historiques e subjectives de la pratique de courses de longue distance. D abord, l ?chantillon la recherche a ?t? compos? de huit sujets-atl?tes du groupe de courreurs de rue Sport Vida. Ainsi, em m?me temps que nous faisons une analyse socio-historique de cette pratique sportive, nous consid?rons l ensemble des aspects s?cio-culturels et poursuivons la recherche avec comme objectif de comprendre les sens qui lui sont attribu?s par les sjuets-atl?tes, au-del? de l aspect ?conomique et de la consommation. Nous observons que, m?me si l alt?tismo qui est pratiqu? a des aspects competitifs (economiques), les atl?tes cr?ent d autres sens pour continuer a pratiquer ce sport, comme les amiti?s, ?tre ensemble avec les amis. Ils rompent avec la logique d?terministe du sport d?passer la limite du corps, vaincre ? n importe quel prix, d?passer les coll?gues -, en cherchant des moments de solidarit?, un sport sans violence et affectif. Nous percevons n?anmoins des contradictions dans le discours de quelques atl?tes quand confessent que le plus important est l amour du sport, les amiti?s, mais r?clament du manque de sponsorts et d appui pour pouvoir s entrainer tranquillement. Cette recherche a aussi montr? que dans la pratique de ce sport, les atl?tes construisent une obstination, sachant le sacrifice qu il impose au corps, mais cela se transforme en plaisir, excitation et recherche d ?motions fortes. Valeurs ?thiques sont aussi construites et valoris?es dans l atl?tisme, ce qui est observ? lorsque que les sujets-atl?tes critiquent avec v?emence a propos de l usage de substances chimiques par les sportifs. En choisissant l imaginaire radical comme principale inspiration th?orique pour cette recherche, il devient ?vident que le sport peut ?tre ressignifi?, ? partir du moment que cet imaginaire est potencialis? dans l enseignement de l ?ducation physique, porvocant chez les ?l?ves une r?flexion critique sur la soci?t? et sur le sport, qui passe ? ?tre redimensionn? vers la solidarit?, avec d?mocratie et autonomie. Enfin, l ?tude a r?v?l? que le sport d endurance est capable de cr?er des liens sociaux et structurer des relations ? partir de cette pratique
Este estudo prop?e uma leitura do esporte de rendimento, com o aporte te?rico do imagin?rio radical, considerando as dimens?es s?cio-hist?ricas e subjetivas na pr?tica de corridas de longa dist?ncia. De in?cio, a amostra da pesquisa foi composta por oito sujeitos-atletas do grupo de corredores de rua Sport Vida. Assim, ao fazermos uma an?lise s?cio-hist?rica dessa pr?tica esportiva, consideramos em conjunto os aspectos socioculturais e seguimos com o objetivo de compreender os sentidos a ela atribu?dos pelos sujeitos-atletas, para al?m do aspecto econ?mico e do consumo. Observamos que, mesmo o atletismo envolvendo aspectos relacionados ao rendimento, os atletas criam outros sentidos para continuarem desenvolvendo essa pr?tica, como as amizades, o estar juntos com os amigos. Eles rompem com a l?gica determinista do esporte ultrapassar o limite do corpo, vencer a qualquer pre?o, sobrepujar os colegas , buscando momentos de solidariedade, um esporte sem viol?ncia e afetivo. Percebemos, por?m, contradi??es, no discurso de alguns atletas, quando confessam que o mais importante ? o amor pelo esporte, as amizades, mas reclamam da falta de patroc?nio e de apoio para poderem treinar com mais tranq?ilidade. Esta pesquisa tamb?m revelou que, nessa pr?tica, os atletas constroem uma obstina??o, devido ao sacrif?cio que ela imp?e ao corpo, por?m isso ? transformado em prazer, excita??o e busca de fortes emo??es. Valores ?ticos tamb?m s?o constru?dos e valorizados no atletismo, o que ? observado quando os sujeitos-atletas fazem cr?ticas contundentes ao uso de subst?ncias qu?micas por aqueles(as) que o praticam. Ao tomarmos o imagin?rio radical como principal fonte te?rica para esta pesquisa, fica evidente que o esporte pode ser ressignificado, desde que esse imagin?rio seja potencializado no ensino da educa??o f?sica, provocando nos alunos uma reflex?o cr?tica sobre a sociedade e sobre o esporte, que passa a ser direcionado para a solidariedade, com democracia e autonomia. Enfim, o estudo revelou que o esporte de rendimento ? capaz de criar la?os sociais e estruturar rela??es ? sua volta
REIG, BRUNO. "Evaluation des couplages electromagnetiques dans des sous ensembles hyperfrequences tres integres. Etude et developpement des technologies de boitier et de connectique. Comparaison entre la modelisation et l'experimentation." Paris 6, 1999. http://www.theses.fr/1999PA066690.
Повний текст джерелаSaeed, Nausheen. "Automated Gravel Road Condition Assessment : A Case Study of Assessing Loose Gravel using Audio Data." Licentiate thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-36402.
Повний текст джерелаDue to unforeseen circumstances the seminar was postponed from May 7 to 28, as duly stated in the new posting page.
Mattes, Julian. "Invariants statistiques et structurels définis par l'arbre de confinement pour le recalage d'images et l'analyse du mouvement." Université Joseph Fourier (Grenoble), 2000. http://www.theses.fr/2000GRE1A002.
Повний текст джерелаMattes, Julian. "Invariants statistiques et structurels définis par l'arbre de confinement pour le recalage d'images et l'analyse du mouvement." Université Joseph Fourier (Grenoble ; 1971-2015), 2000. http://www.theses.fr/2000GRE10247.
Повний текст джерелаКичигіна, Анастасія Юріївна. "Прогнозування ІМТ за допомогою методів машинного навчання". Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/37413.
Повний текст джерелаThesis: 100 p., 17 tabl., 16 fig., 2 add. and 24 references. The object of the study is the human body mass index. The subject of research is machine learning methods - regression models, ensemble model random forest and neural network. In this paper, a study of the dependence of the human body mass index and the presence of excess body weight on eating and living habits. To build the study, the methods of machine learning and data analysis were used, work was done to identify opportunities to improve the performance of standard models and identified the best model for the implementation of predicting and classification based on the data. The direction of work is in the reduced dimensions of the feature space, selection of the best observations with valid data for better performance of models, as well as in combining different teaching methods and obtaining more effective ensemble models.
Vuk, Vranjković. "Реконфигурабилне архитектуре за хардверску акцелерацију предиктивних модела машинског учења". Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2015. http://www.cris.uns.ac.rs/record.jsf?recordId=94819&source=NDLTD&language=en.
Повний текст джерелаU ovoj disertaciji predstavljene su univerzalne rekonfigurabilnearhitekture grubog stepena granulacije za hardversku implementacijuDT (decision trees), ANN (artificial neural networks) i SVM (support vectormachines) prediktivnih modela kao i homogenih i heterogenihansambala. Korišćenjem ovih arhitektura realizovane su dve vrsteDT modela, dve vrste ANN modela, dve vrste SVM modela i sedamvrsta ansambala na FPGA (field programmable gate arrays) čipu.Eksperimenti, zasnovani na skupovima iz standardne UCI baze skupovaza mašinsko učenje, pokazuju da FPGA implementacija omogućavaznačajno ubrzanje (od 1 do 6 redova veličine) prosečnog vremenapotrebnog za predikciju, u poređenju sa softverskim rešenjima.
This thesis proposes universal coarse-grained reconfigurable computingarchitectures for hardware implementation of decision trees (DTs), artificialneural networks (ANNs), support vector machines (SVMs), andhomogeneous and heterogeneous ensemble classifiers (HHESs). Usingthese universal architectures, two versions of DTs, two versions of SVMs,two versions of ANNs, and seven versions of HHESs machine learningclassifiers, have been implemented in field programmable gate arrays(FPGA). Experimental results, based on datasets of standard UCI machinelearning repository database, show that FPGA implementation providessignificant improvement (1–6 orders of magnitude) in the average instanceclassification time, in comparison with software implementations.
Thames, John Lane. "Advancing cyber security with a semantic path merger packet classification algorithm." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45872.
Повний текст джерелаCiss, Saïp. "Forêts uniformément aléatoires et détection des irrégularités aux cotisations sociales." Thesis, Paris 10, 2014. http://www.theses.fr/2014PA100063/document.
Повний текст джерелаWe present in this thesis an application of machine learning to irregularities in the case of social contributions. These are, in France, all contributions due by employees and companies to the "Sécurité sociale", the french system of social welfare (alternative incomes in case of unemployement, Medicare, pensions, ...). Social contributions are paid by companies to the URSSAF network which in charge to recover them. Our main goal was to build a model that would be able to detect irregularities with a little false positive rate. We, first, begin the thesis by presenting the URSSAF and how irregularities can appear, how can we handle them and what are the data we can use. Then, we talk about a new machine learning algorithm we have developped for, "random uniform forests" (and its R package "randomUniformForest") which are a variant of Breiman "random Forests" (tm), since they share the same principles but in in a different way. We present theorical background of the model and provide several examples. Then, we use it to show, when irregularities are fraud, how financial situation of firms can affect their propensity for fraud. In the last chapter, we provide a full evaluation for declarations of social contributions of all firms in Ile-de-France for year 2013, by using the model to predict if declarations present irregularities or not
Haddad, Karim. "L’Unité Temporelle : une approche pour l’écriture de la durée et de sa quantification." Thesis, Sorbonne université, 2020. http://www.theses.fr/2020SORUL141.
Повний текст джерелаIn this thesis, we will study a new approach in the practice of musical time composition starting from a notational concept dedicated to the writing of duration, rhythm and musical form. This new concept that we call Time Unit opens on several questions and issues structured around three key principles : the notation of Time Units, their operability (the potentiality to yield new musical form) and quantization. After examining these different approaches on time, form, duration and quantization, we shall try to create a new grammar of musical time directed on its syntax and its representation. We shall build tools for rhythmical transformation and production of Time Units. Once this is achieved, we will study the same Time Units under their real time aspect raising the issue of “compositional” and its implications on the scope of musical form. After a thorough study on symbolic rhythm quantization of Time Units structures we will explore the path starting from the conception of a composition, through its state of sketch, to its final state achieved by a “correct” quantization preserving the integrity of its discourse. This will be illustrated with case study examples, from our personal works
Ouali, Abdelkader. "Méthodes hybrides parallèles pour la résolution de problèmes d'optimisation combinatoire : application au clustering sous contraintes." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMC215/document.
Повний текст джерелаCombinatorial optimization problems have become the target of many scientific researches for their importance in solving academic problems and real problems encountered in the field of engineering and industry. Solving these problems by exact methods is often intractable because of the exorbitant time processing that these methods would require to reach the optimal solution(s). In this thesis, we were interested in the algorithmic context of solving combinatorial problems, and the modeling context of these problems. At the algorithmic level, we have explored the hybrid methods which excel in their ability to cooperate exact methods and approximate methods in order to produce rapidly solutions of best quality. At the modeling level, we worked on the specification and the exact resolution of complex problems in pattern set mining, in particular, by studying scaling issues in large databases. On the one hand, we proposed a first parallelization of the DGVNS algorithm, called CPDGVNS, which explores in parallel the different clusters of the tree decomposition by sharing the best overall solution on a master-worker model. Two other strategies, called RADGVNS and RSDGVNS, have been proposed which improve the frequency of exchanging intermediate solutions between the different processes. Experiments carried out on difficult combinatorial problems show the effectiveness of our parallel methods. On the other hand, we proposed a hybrid approach combining techniques of both Integer Linear Programming (ILP) and pattern mining. Our approach is comprehensive and takes advantage of the general ILP framework (by providing a high level of flexibility and expressiveness) and specialized heuristics for data mining (to improve computing time). In addition to the general framework for the pattern set mining, two problems were studied: conceptual clustering and the tiling problem. The experiments carried out showed the contribution of our proposition in relation to constraint-based approaches and specialized heuristics
Hong, Je-Yi, and 洪哲儀. "Study of Stock Index Trend Using Tree-based Ensemble Classification." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/42855550952005440684.
Повний текст джерела靜宜大學
財務與計算數學系
104
Stock price Index and economic factors interact as both causes and effects.From to the view of investment, the trend prediction of Stock Price Index can be used to reduce the risk of investment.Predicting trends of stock market prices has been an interesting topic for many years.However, due to various subjective and objective factors, forecasting the trend of stock market prices index is a very challenging task. In this study, we treated the prediction of stock market price index as the classification problem.There are many machine learning algorithms can be used for classification including Support Vector Machine, Neural Network and so on.However, very few models are not plausible to understand how they work in practical.We applied Tree methods to take advantage of model interpretation and still keep acceptable prediction power.Comparing with traditional tree methods, random forest increases the difficulty of model interpretation.Therefore, we studied multiple trees structure constructed by real data to find meaningful predicting variables and the procedure to find model interpretable with financial meaning. We created new variables base on the distribution of cut-off values constructed from multiple trees and adjusted by known financial facts.For predicting 2013 Taiwan stock values index, we found that DPO is a highly impact factor.And we applied clustering methods in multiple trees model to identify the forest with small amounts of trees which has competitive prediction accuracy comparing with random forest.
Yu, Li, and 游力. "Dynamic Ensemble Decision Tree Learning Algorithm for Network Traffic Classification." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/cyqff7.
Повний текст джерела國立交通大學
網路工程研究所
104
Network traffic classification has already been discussed for decades, which gives us the ability to monitor and detect the applications associated with network traffic. It becomes the essential step of network management and traffic engineering such as QoS control, abnormal detection and ISPs network planing. From the earliest approach, which is the port base classification, to the state of the art practice, which is the machine learning classification. Beside, most of the information technology research and advisory organizations have forecast that we are going to enter the era of big data. We would face the high volume, high velocity and high variety data. And machine learning approach traffic classification has satisfying accuracy with lower computing resources, which meet the requirement of high volume and high velocity of big data. However, most of machine learning based traffic classification researches assume the network environment is stable, which is not true. This assumption makes the classifiers unable to deal with highly variety data, since they do not have the countermeasure of the changes of network environment. In order to address the issue, we proposed the dynamic ensemble decision tree learning algorithm or EDT. Our EDT is able to dynamically update its predicting model without retraining whole model all over again. In the experiment, The testing data are collected in our experimental LTE network. Evaluation shows our algorithm can respond to the new application 24 times faster in average than the original C5.0 decision tree learning algorithm without losing more than 1.02% accuracy. The contribution of this thesis is we proposed a new model for decision tree, giving it the ability to dynamically adjust the model.
Tasi, Wei-Lan, and 蔡維倫. "Improve the Classification Performance for Decision Tree by Population-based Approaches with Ensemble." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/90102780678990829408.
Повний текст джерела華梵大學
資訊管理學系碩士班
97
Data mining techniques have been widely used in prediction or classification problems. The decision trees algorithm (DT) that can provides rule-based tree structure is one of the most popular among them and can be applied to various areas. Nevertheless, different problems may require different parameters when applying DT to build the model and the parameter settings will influence classification result. On the other hand, a dataset may contain many features; however, not all features are beneficial for the model. If the feature selection did not perform may increasing cost and reduce DT learning ability. Therefore, scatter search (SS), genetic algorithm (GA) and particle swarm optimization (PSO) are proposed to select the beneficial subset of features and to obtain the better parameters which will result in a better classifications. The above three meta-heuristic algorithms mentioned above all have their its own strength and weakness. If these algorithms can work together, it is expected that the better results can be obtained. This is so called ensemble. This paper is proposed the ensemble to further enhance the prediction or classification accuracy rate. In order to evaluate the proposed approaches, datasets in UCI (University of California) are planned to evaluate the performance of the proposed approaches. The proposed three meta-heuristic methods-based DT algorithm can find the best parameters and feature subset when face various problems, and provide the higher classification accuracy rate.
Filipe, Daniel José Canelas. "Using tree-based ensemble methods to improve the B2B customer acquisition process in the fashion industry." Master's thesis, 2020. https://hdl.handle.net/10216/132634.
Повний текст джерелаFilipe, Daniel José Canelas. "Using tree-based ensemble methods to improve the B2B customer acquisition process in the fashion industry." Dissertação, 2020. https://hdl.handle.net/10216/132634.
Повний текст джерелаSilvestre, Martinho de Matos. "Three-stage ensemble model : reinforce predictive capacity without compromising interpretability." Master's thesis, 2019. http://hdl.handle.net/10362/71588.
Повний текст джерелаOver the last decade, several banks have developed models to quantify credit risk. In addition to the monitoring of the credit portfolio, these models also help deciding the acceptance of new contracts, assess customers profitability and define pricing strategy. The objective of this paper is to improve the approach in credit risk modeling, namely in scoring models to predict default events. To this end, we propose the development of a three-stage ensemble model that combines the results interpretability of the Scorecard with the predictive power of machine learning algorithms. The results show that ROC index improves 0.5%-0.7% and Accuracy 0%-1% considering the Scorecard as baseline.
Chen, Tzu-Tung, and 陳姿彤. "300-year dendroclimatic reconstructions based on conventional methods and Ensemble Empirical Mode Decomposition using Picea morrisonicola tree rings from central Taiwan." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/76399793270131758250.
Повний текст джерела國立臺灣大學
地質科學研究所
99
Virtually very little dendrochronology data have been reported internationally from Taiwan, despite the existence of many dendrochronologically appropriate tree species. In this study, the potential for reconstruction of local paleoclimate was investigated using multi-century tree-ring chronologies developed from Picea morrisonicola (the endemic Taiwan Spruce). Significant correlations were found against the mean April-June diurnal temperature range (DTR) and against the mean July-September maximum temperature (Tmax). Both of these climate parameters were reconstructed based on the regression relationships. In a related study, a new frequency decomposition method called empirical mode decomposition (EMD), one part of the Hilbert-Huang Transform (HHT), was investigated as an alternative to standard methods of chronology generation in terms of climate signal. A noise assisted version of EMD called ensemble empirical mode decomposition (EEMD) was used to decompose the tree-ring time series into a series of quasi-periodic modes from high to low frequency. Consecutive modes were combined from high to low frequency and compared with the climate data. The combination with the most significant climate relationships was then used to reconstruct the climate parameters. As with the reconstructions using traditional methods of chronology generation, statistics from the reconstructions of DTR and Tmax also passed tests for model skill. The reconstruction statistics and variance explained were similar for both methods of chronology generation, with EEMD chronology having better results in the DTR reconstruction and the traditional chronology having better results in the Tmax reconstruction. Adjusted latewood ring widths show significant (p<0.01) positive correlation against Alishan July-September Tmax. Linear regression of the Alishan Tmax on the tree-ring chronology produced a calibration model that accounted for 23% of the actual Tmax variance. This model was used to reconstruct the July-September Tmax back to A.D. 1636. The reconstruction shows warm periods during 1718-1726, 1908-1916, and 2002-2008. Evidence from comparisons with NCEP-NCAR reanalysis data indicates that the summer climate variability in Taiwan is regulated by processes associated with changes in the Western Pacific Subtropical High (WPSH). In years with less precipitation the WPSH reduces the southwesterly monsoonal flow by extending further westward than in other years. This appears as an anomalous warm and dry summer accompanied with anti-cyclonic motion over the East China Sea. In addition, eight of the ten warmest summers (July-September Tmax) in central Taiwan occurred during El Niño years, indicating a link between Taiwan summer maximum temperatures and ENSO dynamics. The earlywood mean chronology was calibrated against April-June DTR. A calibration model that accounted for 28% of the actual DTR variance was then produced to reconstruct the DTR. The increasing Tmin, which can be attributed to locally increased cloud cover, contributed to the reduction of DTR. The reconstructed DTR has a cycle of period 28 years, showing the variations in solar irradiance possibly due to cloudiness changes.
Kandel, Ibrahem Hamdy Abdelhamid. "A comparative study of tree-based models for churn prediction : a case study in the telecommunication sector." Master's thesis, 2019. http://hdl.handle.net/10362/60302.
Повний текст джерелаIn the recent years the topic of customer churn gains an increasing importance, which is the phenomena of the customers abandoning the company to another in the future. Customer churn plays an important role especially in the more saturated industries like telecommunication industry. Since the existing customers are very valuable and the acquisition cost of new customers is very high nowadays. The companies want to know which of their customers and when are they going to churn to another provider, so that measures can be taken to retain the customers who are at risk of churning. Such measures could be in the form of incentives to the churners, but the downside is the wrong classification of a churners will cost the company a lot, especially when incentives are given to some non-churner customers. The common challenge to predict customer churn will be how to pre-process the data and which algorithm to choose, especially when the dataset is heterogeneous which is very common for telecommunication companies’ datasets. The presented thesis aims at predicting customer churn for telecommunication sector using different decision tree algorithms and its ensemble models.
Elmasry, Mohamed Hani Abdelhamid Mohamed Tawfik. "Machine learning approach for credit score analysis : a case study of predicting mortgage loan defaults." Master's thesis, 2019. http://hdl.handle.net/10362/62427.
Повний текст джерелаTo effectively manage credit score analysis, financial institutions instigated techniques and models that are mainly designed for the purpose of improving the process assessing creditworthiness during the credit evaluation process. The foremost objective is to discriminate their clients – borrowers – to fall either in the non-defaulter group, that is more likely to pay their financial obligations, or the defaulter one which has a higher probability of failing to pay their debts. In this paper, we devote to use machine learning models in the prediction of mortgage defaults. This study employs various single classification machine learning methodologies including Logistic Regression, Classification and Regression Trees, Random Forest, K-Nearest Neighbors, and Support Vector Machine. To further improve the predictive power, a meta-algorithm ensemble approach – stacking – will be introduced to combine the outputs – probabilities – of the afore mentioned methods. The sample for this study is solely based on the publicly provided dataset by Freddie Mac. By modelling this approach, we achieve an improvement in the model predictability performance. We then compare the performance of each model, and the meta-learner, by plotting the ROC Curve and computing the AUC rate. This study is an extension of various preceding studies that used different techniques to further enhance the model predictivity. Finally, our results are compared with work from different authors.
Para gerir com eficácia a análise de risco de crédito, as instituições financeiras desenvolveram técnicas e modelos que foram projetados principalmente para melhorar o processo de avaliação da qualidade de crédito durante o processo de avaliação de crédito. O objetivo final é classifica os seus clientes - tomadores de empréstimos - entre aqueles que tem maior probabilidade de pagar suas obrigações financeiras, e os potenciais incumpridores que têm maior probabilidade de entrar em default. Neste artigo, nos dedicamos a usar modelos de aprendizado de máquina na previsão de defaults de hipoteca. Este estudo emprega várias metodologias de aprendizado de máquina de classificação única, incluindo Regressão Logística, Classification and Regression Trees, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Para melhorar ainda mais o poder preditivo, a abordagem do conjunto de meta-algoritmos - stacking - será introduzida para combinar as saídas - probabilidades - dos métodos acima mencionados. A amostra deste estudo é baseada exclusivamente no conjunto de dados fornecido publicamente pela Freddie Mac. Ao modelar essa abordagem, alcançamos uma melhoria no desempenho do modelo de previsibilidade. Em seguida, comparamos o desempenho de cada modelo e o meta-aprendiz, plotando a Curva ROC e calculando a taxa de AUC. Este estudo é uma extensão de vários estudos anteriores que usaram diferentes técnicas para melhorar ainda mais o modelo preditivo. Finalmente, nossos resultados são comparados com trabalhos de diferentes autores.
Dias, Didier Narciso. "Soil Classification Resorting to Machine Learning Techniques." Master's thesis, 2019. http://hdl.handle.net/10362/125335.
Повний текст джерелаA classificação de solos é o ato de resumir a informação sobre um perfil do solo em uma única classe, da qual é possivel inferir várias propriedades, mesmo com a ausência de conhecimento sobre a área de estudo. Estas classes fazem a comunicação dos solos e de como estes podem ser usados, em áreas como a agricultura e silvicultura, mais simples de perceber. Infelizmente a classificação de solos é dispendiosa, demorada, e requer especialistas para realizar as experiências necessárias para classificar corretamente o solo em causa. A presente tese de mestrado focou-se na avaliação de algoritmos de aprendizagem automática para o problema de classificação de solos, baseada maioritariamente nos atributos intrínsecos destes, na região do México. Foi utilizada uma base de dados contendo 6 760 perfis de solos, os 19 464 horizontes que os constituem, e as propriedades químicas e físicas, como o pH e a percentagem de barro, pertencentes a esses horizontes. Quatro métodos de modelação de dados foram testados (standard depths, n first layers, thickness, e area weighted thickness), tal como diferentes valores para uma imputação baseada em k-Nearest Neighbours. Também foi realizada uma comparação entre algoritmos de aprendizagem automática, nomeadamente Random Forests, Gradient Tree Boosting, Deep Neural Networks e Recurrent Neural Networks. Todas as modelações de dados providenciaram resultados similares, quando propriamente parametrisados, atingindo valores de Kappa de 0.504 e accuracy de 0.554, sendo que o métdodo standard depths obteve uma performance mais consistente. O parâmetro k, referente ao método de imputação, revelou ter pouco impacto na variação dos resultados. O algoritmo Gradient Tree Boosting foi o que obteve melhores resultados, seguido de perto pelo modelo de Random Forests. Os métodos baseados em neurónios tiveram resultados substancialmente piores, nunca superando um valor de Kappa de 0.4.