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Dissertations / Theses on the topic 'Decision tree'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Yu, Peng. "Improving Decision Tree Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT037.

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La modélisation par arbres de décision est reconnue pour son efficacité et sa lisibilité, notamment pour les données structurées. Cette thèse s’attaque à deux défis majeurs : l’interprétabilité des arbres profonds et la gestion des variables catégorielles.Nous présentons l’algorithme Linear Tree- Shap, qui facilite l’explication du processus décisionnel en attribuant des scores d’importance à chaque noeud et variable. Parallèlement, nous proposons un cadre méthodologique pour traiter directement les variables catégorielles, améliorant à la fois la précision et la robustesse du modèle. Notre ap

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2

Shi, Haijian. "Best-first Decision Tree Learning." The University of Waikato, 2007. http://hdl.handle.net/10289/2317.

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In best-first top-down induction of decision trees, the best split is added in each step (e.g. the split that maximally reduces the Gini index). This is in contrast to the standard depth-first traversal of a tree. The resulting tree will be the same, just how it is built is different. The objective of this project is to investigate whether it is possible to determine an appropriate tree size on practical datasets by combining best-first decision tree growth with cross-validation-based selection of the number of expansions that are performed. Pre-pruning, post-pruning, CART-pruning can be perfo

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3

Vella, Alan. "Hyper-heuristic decision tree induction." Thesis, Heriot-Watt University, 2012. http://hdl.handle.net/10399/2540.

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A hyper-heuristic is any algorithm that searches or operates in the space of heuristics as opposed to the space of solutions. Hyper-heuristics are increasingly used in function and combinatorial optimization. Rather than attempt to solve a problem using a fixed heuristic, a hyper-heuristic approach attempts to find a combination of heuristics that solve a problem (and in turn may be directly suitable for a class of problem instances). Hyper-heuristics have been little explored in data mining. This work presents novel hyper-heuristic approaches to data mining, by searching a space of attribute

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4

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 E

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5

Qureshi, Taimur. "Contributions to decision tree based learning." Thesis, Lyon 2, 2010. http://www.theses.fr/2010LYO20051/document.

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Advances in data collection methods, storage and processing technology are providing a unique challenge and opportunity for automated data learning techniques which aim at producing high-level information, or models, from data. A Typical knowledge discovery process consists of data selection, data preparation, data transformation, data mining and interpretation/validation of the results. Thus, we develop automatic learning techniques which contribute to the data preparation, transformation and mining tasks of knowledge discovery. In doing so, we try to improve the prediction accuracy of the ov

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6

Ardeshir, G. "Decision tree simplification for classifier ensembles." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/843022/.

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Design of ensemble classifiers involves three factors: 1) a learning algorithm to produce a classifier (base classifier), 2) an ensemble method to generate diverse classifiers, and 3) a combining method to combine decisions made by base classifiers. With regard to the first factor, a good choice for constructing a classifier is a decision tree learning algorithm. However, a possible problem with this learning algorithm is its complexity which has only been addressed previously in the context of pruning methods for individual trees. Furthermore, the ensemble method may require the learning algo

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7

Ahmad, Amir. "Data Transformation for Decision Tree Ensembles." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508528.

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8

Cai, Jingfeng. "Decision Tree Pruning Using Expert Knowledge." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1158279616.

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9

Wu, Shuning. "Optimal instance selection for improved decision tree." [Ames, Iowa : Iowa State University], 2007.

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10

Sinnamon, Roslyn M. "Binary decision diagrams for fault tree analysis." Thesis, Loughborough University, 1996. https://dspace.lboro.ac.uk/2134/7424.

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This thesis develops a new approach to fault tree analysis, namely the Binary Decision Diagram (BDD) method. Conventional qualitative fault tree analysis techniques such as the "top-down" or "bottom-up" approaches are now so well developed that further refinement is unlikely to result in vast improvements in terms of their computational capability. The BDD method has exhibited potential gains to be made in terms of speed and efficiency in determining the minimal cut sets. Further, the nature of the binary decision diagram is such that it is more suited to Boolean manipulation. The BDD method h

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11

Ho, Colin Kok Meng. "Discretization and defragmentation for decision tree learning." Thesis, University of Essex, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299072.

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12

Kassim, M. E. "Elliptical cost-sensitive decision tree algorithm (ECSDT)." Thesis, University of Salford, 2018. http://usir.salford.ac.uk/47191/.

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Cost-sensitive multiclass classification problems, in which the task of assessing the impact of the costs associated with different misclassification errors, continues to be one of the major challenging areas for data mining and machine learning. The literature reviews in this area show that most of the cost-sensitive algorithms that have been developed during the last decade were developed to solve binary classification problems where an example from the dataset will be classified into only one of two available classes. Much of the research on cost-sensitive learning has focused on inducing d

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13

Yedida, Venkata Rama Kumar Swamy. "Protein Function Prediction Using Decision Tree Technique." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1216313412.

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14

Badulescu, Laviniu Aurelian. "ATTRIBUTE SELECTION MEASURE IN DECISION TREE GROWING." Universitaria Publishing House, 2007. http://hdl.handle.net/10150/105610.

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One of the major tasks in Data Mining is classification. The growing of Decision Tree from data is a very efficient technique for learning classifiers. The selection of an attribute used to split the data set at each Decision Tree node is fundamental to properly classify objects; a good selection will improve the accuracy of the classification. In this paper, we study the behavior of the Decision Trees induced with 14 attribute selection measures over three data sets taken from UCI Machine Learning Repository.

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15

Barros, Rodrigo Coelho. "On the automatic design of decision-tree induction algorithms." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21032014-144814/.

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Decision-tree induction is one of the most employed methods to extract knowledge from data. There are several distinct strategies for inducing decision trees from data, each one presenting advantages and disadvantages according to its corresponding inductive bias. These strategies have been continuously improved by researchers over the last 40 years. This thesis, following recent breakthroughs in the automatic design of machine learning algorithms, proposes to automatically generate decision-tree induction algorithms. Our proposed approach, namely HEAD-DT, is based on the evolutionary algorith

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Tsang, Pui-kwan Smith, and 曾沛坤. "Efficient decision tree building algorithms for uncertain data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41290719.

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17

Reay, Karen A. "Efficient fault tree analysis using binary decision diagrams." Thesis, Loughborough University, 2002. https://dspace.lboro.ac.uk/2134/7579.

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The Binary Decision Diagram (BDD) method has emerged as an alternative to conventional techniques for performing both qualitative and quantitative analysis of fault trees. BDDs are already proving to be of considerable use in reliability analysis, providing a more efficient means of analysing a system, without the need for the approximations previously used in the traditional approach of Kinetic Tree Theory. In order to implement this technique, a BDD must be constructed from the fault tree, according to some ordering of the fault tree variables. The selected variable ordering has a crucial ef

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18

Федоров, Д. П. "Comparison of classifiers based on the decision tree." Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/16430.

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The main purpose of this work is to compare classifiers. Random Forest and XGBoost are two popular machine learning algorithms. In this paper, we looked at how they work, compared their features, and obtained accurate results from their robots.

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19

Igboamalu, Frank Nonso. "Decision tree classifiers for incident call data sets." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27076.

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Information technology (IT) has become one of the key technologies for economic and social development in any organization. Therefore the management of Information technology incidents, and particularly in the area of resolving the problem very fast, is of concern to Information technology managers. Delays can result when incorrect subjects are assigned to Information technology incident calls: because the person sent to remedy the problem has the wrong expertise or has not brought with them the software or hardware they need to help that user. In the case study used for this work, there are n

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20

Yenco, Aileen C. "Decision Tree for Ground Improvement in Transportation Applications." University of Akron / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=akron1384435786.

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Tsang, Pui-kwan Smith. "Efficient decision tree building algorithms for uncertain data." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B41290719.

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22

Shah, Hamzei G. Hossein. "Decision tree learning for intelligent mobile robot navigation." Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/6968.

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The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of

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Wickramarachchi, Darshana Chitraka. "Oblique decision trees in transformed spaces." Thesis, University of Canterbury. Mathematics and Statistics, 2015. http://hdl.handle.net/10092/11051.

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Decision trees (DTs) play a vital role in statistical modelling. Simplicity and interpretability of the solution structure have made the method popular in a wide range of disciplines. In data classification problems, DTs recursively partition the feature space into disjoint sub-regions until each sub-region becomes homogeneous with respect to a particular class. Axis parallel splits, the simplest form of splits, partition the feature space parallel to feature axes. However, for some problem domains DTs with axis parallel splits can produce complicated boundary structures. As an alternative, ob

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24

Zhou, Guoqing. "Co-Location Decision Tree for Enhancing Decision-Making of Pavement Maintenance and Rehabilitation." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/26059.

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A pavement management system (PMS) is a valuable tool and one of the critical elements of the highway transportation infrastructure. Since a vast amount of pavement data is frequently and continuously being collected, updated, and exchanged due to rapidly deteriorating road conditions, increased traffic loads, and shrinking funds, resulting in the rapid accumulation of a large pavement database, knowledge-based expert systems (KBESs) have therefore been developed to solve various transportation problems. This dissertation presents the development of theory and algorithm for a new decision tree

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25

Chang, Namsik. "Knowledge discovery in databases with joint decision outcomes: A decision-tree induction approach." Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187227.

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Inductive symbolic learning algorithms have been used successfully over the years to build knowledge-based systems. One of these, a decision-tree induction algorithm, has formed the central component in several commercial packages because of its particular efficiency, simplicity, and popularity. However, the decision-tree induction algorithms developed thus far are limited to domains where each decision instance's outcome belongs to only a single decision outcome class. Their goal is merely to specify the properties necessary to distinguish instances pertaining to different decision outcome cl

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26

Hari, Vijaya. "Empirical Investigation of CART and Decision Tree Extraction from Neural Networks." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1235676338.

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27

Flöter, André. "Analyzing biological expression data based on decision tree induction." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=978444728.

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28

Rangwala, Maimuna H. "Empirical investigation of decision tree extraction from neural networks." Ohio : Ohio University, 2006. http://www.ohiolink.edu/etd/view.cgi?ohiou1151608193.

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29

Flöter, André. "Analyzing biological expression data based on decision tree induction." Phd thesis, Universität Potsdam, 2005. http://opus.kobv.de/ubp/volltexte/2006/641/.

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<P>Modern biological analysis techniques supply scientists with various forms of data. One category of such data are the so called "expression data". These data indicate the quantities of biochemical compounds present in tissue samples.</P> <P>Recently, expression data can be generated at a high speed. This leads in turn to amounts of data no longer analysable by classical statistical techniques. Systems biology is the new field that focuses on the modelling of this information.</P> <P>At present, various methods are used for this purpose. One superordinate class of these methods is machine l

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30

Gao, Ying. "using decision tree to analyze the turnover of employees." Thesis, Uppsala universitet, Institutionen för informatik och media, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325113.

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31

Sjunnebo, Joakim. "Application of the Boosted Decision Tree Algorithmto Waveform Discrimination." Thesis, KTH, Fysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-129408.

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The Polarised Gamma-ray Observer (PoGOLite) is a balloon-borne experiment aimed at measuring the polarisation of hard X-rays from astronomical sources. In the planned flight environment the neutron background is high. A smaller version of PoGOLite, named PoGOLino, was constructed with the goal of measuring the neutron background rates and was launched in March 2013. The signals produced in the detectors of both these instruments give rise to waveforms of different shapes depending on the type of detector the interaction occurred in. A method to distinguish between signal and background wavefor

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32

SOBRAL, ANA PAULA BARBOSA. "HOURLY LOAD FORECASTING A NEW APPROACH THROUGH DECISION TREE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3710@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>A importância da previsão de carga a curto prazo (até uma semana à frente) em crescido recentemente. Com os processos de privatização e implantação de ompetição no setor elétrico brasileiro, a previsão de tarifas de energia vai se tornar extremamente importante. As previsões das cargas elétricas são fundamentais para alimentar as ferramentas analíticas utilizadas na sinalização das tarifas. Em conseqüência destas mudanças estruturais no setor, a variabilidade e a não-estacionaridade das cargas elétricas tendem a aumentar

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MARQUES, DANIEL DOS SANTOS. "A DECISION TREE LEARNER FOR COST-SENSITIVE BINARY CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28239@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Problemas de classificação foram amplamente estudados na literatura de aprendizado de máquina, gerando aplicações em diversas áreas. No entanto, em diversos cenários, custos por erro de classificação podem variar bastante, o que motiva o estudo de técnicas de classificação sensível ao custo. Nesse trabalho, discutimos o uso de árvores de decisão para o problema mais geral de Aprendizado Sensível ao Custo do Exemplo (ASCE), onde os custos dos erros de classificação variam com o

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Булах, В. А., Л. О. Кіріченко, and Т. А. Радівілова. "Classification of Multifractal Time Series by Decision Tree Methods." Thesis, КНУ, 2018. http://openarchive.nure.ua/handle/document/5840.

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The article considers classification task of model fractal time series by the methods of machine learning. To classify the series, it is proposed to use the meta algorithms based on decision trees. To modeling the fractal time series, binomial stochastic cascade processes are used. Classification of time series by the ensembles of decision trees models is carried out. The analysis indicates that the best results are obtained by the methods of bagging and random forest which use regression trees.

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35

Azad, Mohammad. "Decision and Inhibitory Trees for Decision Tables with Many-Valued Decisions." Diss., 2018. http://hdl.handle.net/10754/628023.

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Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set of decision trees for the given decision table which was previously only attainable by brute-force algorithms. We study decision tables with many-valued decisions (each row may contain multiple decisions) because they are more reasonable models of data in many cases. To address this problem in a broad sense, we consider not only decision trees but

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Boz, Olcay. "Converting a trained neural network to a decision tree dectext-decision tree extractor /." Diss., 2000. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:9982861.

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YU, CHIH-FENG, and 余致鋒. "Application of Decision Tree C5.0 to Fund Decision." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/y98nsm.

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碩士<br>國立嘉義大學<br>企業管理學系<br>106<br>In recent years, financial literacy of citizens has been improving. Furthermore, financial investment channels have likewise multiplied. Most investment tools all need a lot of financial know-how in order to obtain steady profits. Compared to other financial tools, mutual fund risks and barriers to entry are relatively low. The total number of kinds of mutual funds have been increasing yearly and within the many mutual funds available, picking the right fund and strategy to take as the best investment methods are what investors focus on. Every mutual fund has a

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Huang, Xiao-Juan, and 黃小娟. "Decision-Tree Based Image Clustering." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/42912242158073405104.

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碩士<br>南華大學<br>資訊管理學系碩士班<br>90<br>In this thesis, we propose an image clustering method based on CLTree for image segmentation. CLTree is a clustering algorithm that uses decision-tree technique. It’s quit different from existing clustering methods, and it finds clusters without making any prior assumptions or any input parameters. Whether a clustering is good or bad depends on the user's subjective judgment, so we offer three image segmentation results. The experimental results reveal that all of them

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Wu, Chia-Chi, and 吳家齊. "Resource-Constrained Decision Tree Induction." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/57990131846994037048.

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博士<br>國立中央大學<br>資訊管理研究所<br>98<br>Classification is one of the most important research domains in data mining. Among the existing classifiers, decision trees are probably the most popular and commonly-used classification models. Most of the decision tree algorithms aimed to maximize the classification accuracy and minimize the classification error. However, in many real-world applications, there are various types of cost or resource consumption involved in both the induction of decision tree and the classification of future instance. Furthermore, the problem we face may require us to complete a

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Jeng, Yung Mo, and 鄭永模. "The Fuzzy Decision Tree Induction." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/11456447856313611299.

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Shi-Feng, Hsi. "The Defuzzification for Fuzzy Decision Tree." 2001. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611304405.

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Hsi, Shi-Feng, and 奚世峰. "The Defuzzification for Fuzzy Decision Tree." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/48869673305420105003.

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碩士<br>元智大學<br>資訊管理研究所<br>89<br>In recent years, fuzzy decision tree had been widely used to extracting classification knowledge from a set of feature-based data. And many researchers are engaged in the more efficient and optimal algorithms to construct fuzzy decision trees. However, very few papers discuss the process of defuzzification in fuzzy decision tree. Therefore, we propose a new method that emphasizes on the defuzzification process. The tree build by our method is called weighted fuzzy decision tree. It uses the concept of weighted fuzzy production rule(WFPR) in defuzzification proces

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Randall, William D. "Software reusability: a decision tree model." Thesis, 1988. http://hdl.handle.net/10945/23120.

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Lin, Cheng-ying, and 林政頴. "Privacy Preserving for Distributed Decision Tree." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/01048697791498275075.

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碩士<br>國立臺南大學<br>數位學習科技學系碩士班<br>96<br>As the recent development of the computer science, the data quantity of enterprise database increases rapidly. To extract the usefulness information from huge databases, many efficient data mining technologies have been applied. In recent years, the data mining tools are more and more powerful, and the risk of privacy leak has become an urgent problem. Privacy preserving data mining is a relatively new research area in data mining and knowledge discovery. In a common situation, databases are distributed among several organizations who would like to cooperat

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Chen, Yih-Ming, and 陳奕名. "Borderline SMOTE adaptive boosted decision tree." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/02768976104039544520.

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碩士<br>國立交通大學<br>統計學研究所<br>104<br>The problem of learning from imbalanced data has been receiving a growing attention. Since dealing with imbalanced data may decrease the efficiency of classifier, many researchers have been working on this domain and coming up with many solutions, such as the method of combining SMOTE(Synthetic Minority Over-sampling Technique) and decision tree. In this study, we review the existing methods including SMOTE, Borderline SMOTE, Adaptive Boosting and SMOTE Boosting. To improve these methods, we propose an approach Borderline SMOTE Boosting. This approach is compar

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Ngan, Dang Thi Kim, and 鄧氏金銀. "HTTP Botnet detection using decision tree." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/78666177227649974399.

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碩士<br>中國文化大學<br>資訊管理學系<br>102<br>Botnet is the most dangerous and widespread threat among the diverse forms of malware internet-attacks nowaday. A botnet is a group of damaged computers connected via Internet which are remotely accessed and controlled by hackers to make various network attacks. Malicious activities include DDoS attack, spam, click fraud, identity theft and information phishing. The most basic characteristic of botnets is the use of command and control channels to communicate with botnet and through which bonet can be updated and command. Botnet has become a common and effectiv

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Lai, Jian-Cheng, and 賴建丞. "Fast Quad-Tree Depth Decision Algorithm for HEVC Coding Tree Block." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/39ucm4.

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碩士<br>國立虎尾科技大學<br>資訊工程研究所<br>102<br>High Efficiency Video Coding (HEVC) is recently developed for ultra high definition video compression technique, which provides a higher compression ratio and throughput compared with previously video compression standard H.264/AVC. Therefore, this technique is widely used to limited bandwidth network transmission and confined storage space. In order to obtain the higher compression ratio and maintain video quality, which provides variable block partition and mode prediction for HEVC encoder. If each block is computed during the mode decision process, a lot

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Chao-YenChien and 簡兆彥. "Building Balanced Search Tree based on Layered Decision Tree for Packet Classification." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/45153250848607847087.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>100<br>Packet classification is an important building block of the Internet routers for many network applications, such as Quality of Service (QoS), security, monitoring, analysis, and network intrusion detection (NIDS). In this thesis, we propose a scheme called Layer based Search Tree (LST) to solve multi-field packet classification problem. LST improves the traditional decision tree based schemes (e.g. HyperCuts and EffiCuts) by reconstructing the leaf nodes of the decision tree as an approximately balanced search tree. Since all the address subspace covered by

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Yang, Tsan-Hui, and 楊璨輝. "Behavior Cloning by RL-based Decision Tree." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/32882692325935020525.

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碩士<br>國立中正大學<br>電機工程所<br>95<br>It is hard to define a state space or the proper reward function in reinforcement learning to make the robot act as expected. In this paper, we demonstrate the expected behavior for a robot. Then a RL-based decision tree approach which decides to split according to long–term evaluations, instead of a top-down greedy strategy which finds out the relationship between the input and output from the demonstration data. We use this method to teach a robot for target seeking problem. In order to promote the performance in tackling target seeking problem, we add a Q-lea

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Shao, Fu-Hsiang, and 邵福祥. "The kalman filter embedded fuzzy decision tree." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/84437871359722247166.

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