Relevant bibliographies by topics / Fuzzy decision tree

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  2. Dissertations / Theses
  3. Books
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  5. Conference papers

Academic literature on the topic 'Fuzzy decision tree'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

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Journal articles on the topic "Fuzzy decision tree"

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Yun, Jooyeol, Jun won Seo, and Taeseon Yoon. "Fuzzy Decision Tree." International Journal of Fuzzy Logic Systems 4, no. 3 (2014): 7–11. http://dx.doi.org/10.5121/ijfls.2014.4302.

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Cai, Yuliang, Huaguang Zhang, Qiang He, and Shaoxin Sun. "New classification technique: fuzzy oblique decision tree." Transactions of the Institute of Measurement and Control 41, no. 8 (2018): 2185–95. http://dx.doi.org/10.1177/0142331218774614.

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Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put into an additional node – the only non-leaf node in this layer. Construction of the FODT consists of four major steps: (a) generation of fuzzy membership functions automatically by AFS theory according to the raw data distribution; (b) extraction of dynamically fuzzy rules in each non-leaf node by the fuzzy rule extraction algorithm (FREA); (c) construction of the FODT by the fuzzy rules obtained from step (b); and (d) determination of the optimal threshold [Formula: see text] to generate a final tree. Compared with five traditional decision trees (C4.5, LADtree (LAD), Best-first tree (BFT), SimpleCart (SC) and NBTree (NBT)) and a recently obtained fuzzy rules decision tree (FRDT) on eight UCI machine learning data sets and one biomedical data set (ALLAML), the experimental results demonstrate that the proposed algorithm outperforms the other decision trees in both classification accuracy and tree size.
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Mu, Yashuang, Lidong Wang, and Xiaodong Liu. "Dynamic programming based fuzzy partition in fuzzy decision tree induction." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 6757–72. http://dx.doi.org/10.3233/jifs-191497.

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Fuzzy decision trees are one of the most popular extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Among the majority of fuzzy decision trees learning methods, the number of fuzzy partitions is given in advance, that is, there are the same amount of fuzzy items utilized in each condition attribute. In this study, a dynamic programming-based partition criterion for fuzzy items is designed in the framework of fuzzy decision tree induction. The proposed criterion applies an improved dynamic programming algorithm used in scheduling problems to establish an optimal number of fuzzy items for each condition attribute. Then, based on these fuzzy partitions, a fuzzy decision tree is constructed in a top-down recursive way. A comparative analysis using several traditional decision trees verify the feasibility of the proposed dynamic programming based fuzzy partition criterion. Furthermore, under the same framework of fuzzy decision trees, the proposed fuzzy partition solution can obtain a higher classification accuracy than some cases with the same amount of fuzzy items.
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BHATT, RAJEN B., and M. GOPAL. "NEURO-FUZZY DECISION TREES." International Journal of Neural Systems 16, no. 01 (2006): 63–78. http://dx.doi.org/10.1142/s0129065706000470.

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Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are often criticized to result in poor learning accuracy. In this paper, we propose Neuro-Fuzzy Decision Trees (N-FDTs); a fuzzy decision tree structure with neural like parameter adaptation strategy. In the forward cycle, we construct fuzzy decision trees using any of the standard induction algorithms like fuzzy ID3. In the feedback cycle, parameters of fuzzy decision trees have been adapted using stochastic gradient descent algorithm by traversing back from leaf to root nodes. With this strategy, during the parameter adaptation stage, we keep the hierarchical structure of fuzzy decision trees intact. The proposed approach of applying backpropagation algorithm directly on the structure of fuzzy decision trees improves its learning accuracy without compromising the comprehensibility (interpretability). The proposed methodology has been validated using computational experiments on real-world datasets.
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Li, Wei, Xiaoyu Ma, Yumin Chen, et al. "Random Fuzzy Granular Decision Tree." Mathematical Problems in Engineering 2021 (June 9, 2021): 1–17. http://dx.doi.org/10.1155/2021/5578682.

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In this study, the classification problem is solved from the view of granular computing. That is, the classification problem is equivalently transformed into the fuzzy granular space to solve. Most classification algorithms are only adopted to handle numerical data; random fuzzy granular decision tree (RFGDT) can handle not only numerical data but also nonnumerical data like information granules. Measures can be taken in four ways as follows. First, an adaptive global random clustering (AGRC) algorithm is proposed, which can adaptively find the optimal cluster centers and maximize the ratio of interclass standard deviation to intraclass standard deviation, and avoid falling into local optimal solution; second, on the basis of AGRC, a parallel model is designed for fuzzy granulation of data to construct granular space, which can greatly enhance the efficiency compared with serial granulation of data; third, in the fuzzy granular space, we design RFGDT to classify the fuzzy granules, which can select important features as tree nodes based on information gain ratio and avoid the problem of overfitting based on the pruning algorithm proposed. Finally, we employ the dataset from UC Irvine Machine Learning Repository for verification. Theory and experimental results prove that RFGDT has high efficiency and accuracy and is robust in solving classification problems.
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Sakurai, Shigeaki. "Refinement of fuzzy decision tree." IEEJ Transactions on Electronics, Information and Systems 117, no. 12 (1997): 1833–39. http://dx.doi.org/10.1541/ieejeiss1987.117.12_1833.

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Yang, Shiueng-Bien. "Fuzzy variable-branch decision tree." Journal of Electronic Imaging 19, no. 4 (2010): 043012. http://dx.doi.org/10.1117/1.3504357.

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Chandra, B., and P. P. Varghese. "Fuzzy SLIQ Decision Tree Algorithm." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 5 (2008): 1294–301. http://dx.doi.org/10.1109/tsmcb.2008.923529.

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Zhai, Junhai, Xizhao Wang, Sufang Zhang, and Shaoxing Hou. "Tolerance rough fuzzy decision tree." Information Sciences 465 (October 2018): 425–38. http://dx.doi.org/10.1016/j.ins.2018.07.006.

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GASIR, FATHI, KEELEY CROCKETT, and ZUHAIR BANDAR. "INDUCING FUZZY REGRESSION TREE FORESTS USING ARTIFICIAL IMMUNE SYSTEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, supp02 (2012): 133–57. http://dx.doi.org/10.1142/s0218488512400181.

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Fuzzy decision forests aim to improve the predictive power of single fuzzy decision trees by allowing multiple views of the same domain to be modelled. Such forests have been successfully created for classification problems where the outcome field is discrete; however predicting a continuous output value is more challenging in combining the output from multiple fuzzy decision trees. This paper presents a new approach to creating fuzzy regression tree forests based upon the induction of multiple fuzzy regression decision trees from one training sample, where each tree will represent a different view of the data domain. The singular fuzzy regression trees are induced using a proven algorithm known as Elgasir which fuzzifies crisp CHAID decision trees using trapezoidal membership functions for fuzzification and applies Takagi-Sugeno inference to obtain the final predicted values. A modified version of Artificial Immune System Network model (opt-aiNet) is then used for the simultaneous optimization of the membership functions across all trees within the forest. A strength of the proposed method is that data does not require fuzzification before forest induction this reducing pre-processing time and the need for subjective human experts. Five problem sets from the UCI repository and KEEL repository are used to evaluate the approach. The experimental results have shown that fuzzy regression tree forests reduce the error rate compared with single fuzzy regression trees.
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Dissertations / Theses on the topic "Fuzzy decision tree"

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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 training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed.
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Pavuluri, Manoj Kumar. "Fuzzy decision tree classification for high-resolution satellite imagery /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p1418056.

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Abidin, Haji Izham Haji Zainal. "The application of fuzzy decision tree for voltage collapse analysis." Thesis, University of Strathclyde, 2002. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=20372.

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In the time of rapid growth, there is an increase of demand for a reliable and stable power supply. Due to this, utility companies are forced to operate their power system nearer to its maximum capabilities since system expansion may be a costly option. As a result, the power system will be at risk to voltage collapse. Voltage collapse phenomenon is known to be complex and localised in nature but with a widespread effect. The ultimate effect of voltage collapse would be total system collapse which would incur high losses to utility companies. This thesis discusses the voltage collapse phenomenon, its causes, effects and its analytical tools. Looking into its analytical tools, it is observed that it relies upon system equations and models. Published results from these techniques are accurate but may require long computation time for a big and complex system. As a possible solution, this thesis looks into combining machine learning techniques with fuzzy logic in creating a fuzzy decision tree (FDT) tool for voltage collapse analysis. The algorithm utilises static power flow solution as data sets in partitioning the power system into strong and weak areas. From several test results and algorithm development, this research concludes with a possible voltage collapse analytical tool using a hybrid FDT approach based upon multiple attribute partitioning. This thesis concludes with discussions on test results highlighting the FDT performance and ends with a discussion on possible future development on the FDT in creating a more complete tool for voltage collapse analysis.
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Evans, Liam. "Experience-based decision support methodology for manufacturing technology selection : a fuzzy-decision-tree mining approach." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/13719/.

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Manufacturing companies must invest in new technologies and processes to succeed in a rapidly changing global environment. Managers have the difficulty of justifying capital investment in adopting new, state-of-the-art technology. Technology investment accounts for a large part of capital spending and is a key form of improving competitive advantage. Typical approaches focus on the expected return of investment and financial reward gained from the implementation of such equipment. With an increasingly dynamic market environment and global economic model, forecasting of financial payback can be argued to become increasingly less accurate. Subsequently, less quantifiable factors are becoming increasingly important. For example, the alignment of a technology with an organisations objective to fulfil future potential and gain competitive advantage is becoming as crucial as economic evaluation. In addition, the impact on human operators and skill level required must be considered. This research was motivated by the lack of decision methodologies that understand why a technology is more successful within an environment rather than re-examining the underlying performance attributes of a technology. The aim is to create a common approach where both experts and non-experts can use historical decision information to support the evaluation and selection of an optimal manufacturing technology. This form of approach is based on the logic in which a decision maker would irrationally recall previous decisions to identify relationships with new problem cases. The work investigates data mining and machine learning techniques to discover the underlying influences to improve technology selection under a set of dynamic factors. The approach initially discovers the practices to which an expert would conduct the selection of a manufacturing technology within industry. A defined understanding of the problem and techniques was subsequently concluded. This led to an understanding of the structure by which historical decision information is recalled by an expert to support new selection problems. The key attributes in the representation of a case were apparent and a form of characterising tangible and intangible variables was justified. This led to the development of a novel, experience-based manufacturing technology selection framework using fuzzy-decision-trees. The methodology is an iterative approach of learning from previously implemented technology cases. Rules and underlying knowledge of the relationships in past cases predicts the outcome of new decision problems. The link of information from a multitude of historical cases may identify those technologies with technical characteristics that perform optimally for projects with unique requirements. This also indicates the likeliness of technologies performing successfully based on the project requirements. Historical decision cases are represented through original project objectives, technical performance attributes of the chosen technology and judged project performance. The framework was shown to provide a comprehensive foundation for decision support that reduces the uncertainty and subjective influence within the selection process. The model was developed with industrial guidance to represent the actions of a manufacturing expert. The performance of the tool was measured by industrial experts. The approach was found to represent well the decision logic of a human expert based on their developed experience through cases. The application to an industrial decision case study demonstrated encouraging results and use by decision makers feasible. The model reduces the subjectivity in the process by using case information that is formed from multiple experts of a prior decision case. The model is applied in a shorter time period than existing practices and the ranking of potential solutions is well aligned to the understanding of a decision maker. To summarise, this research highlights the importance of focusing on less quantifiable factors and the performance of a technology to a specific problem/environment. The arrangement of case information thus represents the experience an expert would acquire and recall as part of the decision process.
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Li, XuQin. "Data mining using intelligent systems : an optimized weighted fuzzy decision tree approach." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/38159/.

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Data mining can be said to have the aim to analyze the observational datasets to find relationships and to present the data in ways that are both understandable and useful. In this thesis, some existing intelligent systems techniques such as Self-Organizing Map, Fuzzy C-means and decision tree are used to analyze several datasets. The techniques are used to provide flexible information processing capability for handling real-life situations. This thesis is concerned with the design, implementation, testing and application of these techniques to those datasets. The thesis also introduces a hybrid intelligent systems technique: Optimized Weighted Fuzzy Decision Tree (OWFDT) with the aim of improving Fuzzy Decision Trees (FDT) and solving practical problems. This thesis first proposes an optimized weighted fuzzy decision tree, incorporating the introduction of Fuzzy C-Means to fuzzify the input instances but keeping the expected labels crisp. This leads to a different output layer activation function and weight connection in the neural network (NN) structure obtained by mapping the FDT to the NN. A momentum term was also introduced into the learning process to train the weight connections to avoid oscillation or divergence. A new reasoning mechanism has been also proposed to combine the constructed tree with those weights which had been optimized in the learning process. This thesis also makes a comparison between the OWFDT and two benchmark algorithms, Fuzzy ID3 and weighted FDT. SIx datasets ranging from material science to medical and civil engineering were introduced as case study applications. These datasets involve classification of composite material failure mechanism, classification of electrocorticography (ECoG)/Electroencephalogram (EEG) signals, eye bacteria prediction and wave overtopping prediction. Different intelligent systems techniques were used to cluster the patterns and predict the classes although OWFDT was used to design classifiers for all the datasets. In the material dataset, Self-Organizing Map and Fuzzy C-Means were used to cluster the acoustic event signals and classify those events to different failure mechanism, after the classification, OWFDT was introduced to design a classifier in an attempt to classify acoustic event signals. For the eye bacteria dataset, we use the bagging technique to improve the classification accuracy of Multilayer Perceptrons and Decision Trees. Bootstrap aggregating (bagging) to Decision Tree also helped to select those most important sensors (features) so that the dimension of the data could be reduced. Those features which were most important were used to grow the OWFDT and the curse of dimensionality problem could be solved using this approach. The last dataset, which is concerned with wave overtopping, was used to benchmark OWFDT with some other Intelligent Systems techniques, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Evolving Fuzzy Neural Network (EFuNN), Genetic Neural Mathematical Method (GNMM) and Fuzzy ARTMAP. Through analyzing these datasets using these Intelligent Systems Techniques, it has been shown that patterns and classes can be found or can be classified through combining those techniques together. OWFDT has also demonstrated its efficiency and effectiveness as compared with a conventional fuzzy Decision Tree and weighted fuzzy Decision Tree.
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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 overall learning process. Our work focuses on decision tree based learning and thus, we introduce various preprocessing and transformation techniques such as discretization, fuzzy partitioning and dimensionality reduction to improve this type of learning. However, these techniques can be used in other learning methods e.g. discretization can also be used for naive-bayes classifiers. The data preparation step represents almost 80 percent of the problem and is both time consuming and critical for the quality of modeling. Discretization of continuous features is an important problem that has effects on accuracy, complexity, variance and understandability of the induction models. In this thesis, we propose and develop resampling based aggregation techniques that improve the quality of discretization. Later, we validate by comparing with other discretization techniques and with an optimal partitioning method on 10 benchmark data sets.The second part of our thesis concerns with automatic fuzzy partitioning for soft decision tree induction. Soft or fuzzy decision tree is an extension of the classical crisp tree induction such that fuzzy logic is embedded into the induction process with the effect of more accurate models and reduced variance, but still interpretable and autonomous. We modify the above resampling based partitioning method to generate fuzzy partitions. In addition we propose, develop and validate another fuzzy partitioning method that improves the accuracy of the decision tree.Finally, we adopt a topological learning scheme and perform non-linear dimensionality reduction. We modify an existing manifold learning based technique and see whether it can enhance the predictive power and interpretability of classification<br>La recherche avancée dans les méthodes d'acquisition de données ainsi que les méthodes de stockage et les technologies d'apprentissage, s'attaquent défi d'automatiser de manière systématique les techniques d'apprentissage de données en vue d'extraire des connaissances valides et utilisables.La procédure de découverte de connaissances s'effectue selon les étapes suivants: la sélection des données, la préparation de ces données, leurs transformation, le fouille de données et finalement l'interprétation et validation des résultats trouvés. Dans ce travail de thèse, nous avons développé des techniques qui contribuent à la préparation et la transformation des données ainsi qu'a des méthodes de fouille des données pour extraire les connaissances. A travers ces travaux, on a essayé d'améliorer l'exactitude de la prédiction durant tout le processus d'apprentissage. Les travaux de cette thèse se basent sur les arbres de décision. On a alors introduit plusieurs approches de prétraitement et des techniques de transformation; comme le discrétisation, le partitionnement flou et la réduction des dimensions afin d'améliorer les performances des arbres de décision. Cependant, ces techniques peuvent être utilisées dans d'autres méthodes d'apprentissage comme la discrétisation qui peut être utilisées pour la classification bayesienne.Dans le processus de fouille de données, la phase de préparation de données occupe généralement 80 percent du temps. En autre, elle est critique pour la qualité de la modélisation. La discrétisation des attributs continus demeure ainsi un problème très important qui affecte la précision, la complexité, la variance et la compréhension des modèles d'induction. Dans cette thèse, nous avons proposes et développé des techniques qui ce basent sur le ré-échantillonnage. Nous avons également étudié d'autres alternatives comme le partitionnement flou pour une induction floue des arbres de décision. Ainsi la logique floue est incorporée dans le processus d'induction pour augmenter la précision des modèles et réduire la variance, en maintenant l'interprétabilité.Finalement, nous adoptons un schéma d'apprentissage topologique qui vise à effectuer une réduction de dimensions non-linéaire. Nous modifions une technique d'apprentissage à base de variété topologiques `manifolds' pour savoir si on peut augmenter la précision et l'interprétabilité de la classification
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Gerdes, Mike. "Predictive Health Monitoring for Aircraft Systems using Decision Trees." Licentiate thesis, Linköpings universitet, Fluida och mekatroniska system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105843.

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Unscheduled aircraft maintenance causes a lot problems and costs for aircraft operators. This is due to the fact that aircraft cause significant costs if flights have to be delayed or canceled and because spares are not always available at any place and sometimes have to be shipped across the world. Reducing the number of unscheduled maintenance is thus a great costs factor for aircraft operators. This thesis describes three methods for aircraft health monitoring and prediction; one method for system monitoring, one method for forecasting of time series and one method that combines the two other methods for one complete monitoring and prediction process. Together the three methods allow the forecasting of possible failures. The two base methods use decision trees for decision making in the processes and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have the advantage that the generated code can be fast and easily processed, they can be altered by human experts without much work and they are readable by humans. The human readability and modification of the results is especially important to include special knowledge and to remove errors, which the automated code generation produced.
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Abu-halaweh, Nael Mohammed. "Integrating Information Theory Measures and a Novel Rule-Set-Reduction Tech-nique to Improve Fuzzy Decision Tree Induction Algorithms." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/cs_diss/48.

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Machine learning approaches have been successfully applied to many classification and prediction problems. One of the most popular machine learning approaches is decision trees. A main advantage of decision trees is the clarity of the decision model they produce. The ID3 algorithm proposed by Quinlan forms the basis for many of the decision trees’ application. Trees produced by ID3 are sensitive to small perturbations in training data. To overcome this problem and to handle data uncertainties and spurious precision in data, fuzzy ID3 integrated fuzzy set theory and ideas from fuzzy logic with ID3. Several fuzzy decision trees algorithms and tools exist. However, existing tools are slow, produce a large number of rules and/or lack the support for automatic fuzzification of input data. These limitations make those tools unsuitable for a variety of applications including those with many features and real time ones such as intrusion detection. In addition, the large number of rules produced by these tools renders the generated decision model un-interpretable. In this research work, we proposed an improved version of the fuzzy ID3 algorithm. We also introduced a new method for reducing the number of fuzzy rules generated by Fuzzy ID3. In addition we applied fuzzy decision trees to the classification of real and pseudo microRNA precursors. Our experimental results showed that our improved fuzzy ID3 can achieve better classification accuracy and is more efficient than the original fuzzy ID3 algorithm, and that fuzzy decision trees can outperform several existing machine learning algorithms on a wide variety of datasets. In addition our experiments showed that our developed fuzzy rule reduction method resulted in a significant reduction in the number of produced rules, consequently, improving the produced decision model comprehensibility and reducing the fuzzy decision tree execution time. This reduction in the number of rules was accompanied with a slight improvement in the classification accuracy of the resulting fuzzy decision tree. In addition, when applied to the microRNA prediction problem, fuzzy decision tree achieved better results than other machine learning approaches applied to the same problem including Random Forest, C4.5, SVM and Knn.
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Lopes, Mariana Vieira Ribeiro. "Tratamento de imprecisão na geração de árvores de decisão." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8954.

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Submitted by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T20:30:11Z No. of bitstreams: 1 DissMVRL.pdf: 2179441 bytes, checksum: 3c4089c4b24a3d98521f8561c6f2c515 (MD5)<br>Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T20:30:33Z (GMT) No. of bitstreams: 1 DissMVRL.pdf: 2179441 bytes, checksum: 3c4089c4b24a3d98521f8561c6f2c515 (MD5)<br>Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T20:30:39Z (GMT) No. of bitstreams: 1 DissMVRL.pdf: 2179441 bytes, checksum: 3c4089c4b24a3d98521f8561c6f2c515 (MD5)<br>Made available in DSpace on 2017-08-08T20:31:24Z (GMT). No. of bitstreams: 1 DissMVRL.pdf: 2179441 bytes, checksum: 3c4089c4b24a3d98521f8561c6f2c515 (MD5) Previous issue date: 2016-03-03<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>Inductive Decision Trees (DT) are mechanisms based on the symbolic paradigm of machine learning which main characteristics are easy interpretability and low computational cost. Though they are widely used, the DTs can represent problems with just discrete or continuous variables. However, for some problems, the variables are not well represented in this way. In order to improve DTs, the Fuzzy Decision Trees (FDT) were developed, adding the ability to deal with fuzzy variables to the Inductive Decision Trees, making them capable to deal with imprecise knowledge. In this text, it is presented a new algorithm for fuzzy decision trees induction. Its fuzification method is applied during the induction and it is inspired by the C4.5’s partitioning method for continuous attributes. The proposed algorithm was tested with 20 datasets from UCI repository (LICHMAN, 2013). It was compared with other three algorithms that implement different solutions to classification problem: C4.5, which induces an Inductive Decision Tree, FURIA, that induces a Rule-based Fuzzy System and FuzzyDT, which induces a Fuzzy Decision Tree where the fuzification is done before tree’s induction is performed. The results are presented in Chapter 4.<br>As Árvores de Decisão Indutivas (AD) são um mecanismo baseado no paradigma simbólico do Aprendizado de Máquina que tem como principais características a fácil interpretabilidade e baixo custo computacional. Ainda que sejam amplamente utilizadas, as ADs são limitadas à representação de problemas cujas variáveis são do tipo discreto ou contínuo. No entanto, para alguns tipos de problemas, pode haver variáveis que não são bem representadas por estes formatos. Diante deste contexto, foram criadas as Árvores de Decisão Fuzzy (ADF), que adicionam à interpretabilidade das Árvores de Decisão Indutivas, a capacidade de lidar com variáveis fuzzy, as quais representam adequadamente conhecimentos imprecisos. Neste texto, apresentamos o trabalho desenvolvido durante o mestrado, que tem como principal resultado um novo algoritmo para indução de Árvores de Decisão Fuzzy, cujo método de fuzificação dos atributos contínuos é realizado durante a indução da árvore e foi inspirado no método de particionamento de atributos contínuos adotado pelo C4.5. Para validação do algoritmo, foram realizados testes com 20 conjuntos de dados do repositório UCI (LICHMAN, 2013) e o algoritmo foi comparado com outros três algoritmos que abordam o problema de classificação por meio de técnicas diferentes: o C4.5 que induz uma Árvore de Decisão Indutiva, o FURIA, que induz um Sistema Fuzzy Baseado em Regras, porém não segue a estrutura de árvore e o FuzzyDT que induz uma Árvore de Decisão fuzzy realizando a fuzificação dos atributos contínuos antes da indução da árvore. Os resultados dos experimentos realizados são apresentados e discutidos no Capítulo 4 deste texto.
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McWhorter, Tanner Maxwell. "Cognitive Electronic Warfare System." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1595708553000249.

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Books on the topic "Fuzzy decision tree"

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Junhai, Zhai, ed. Ji yu bu que ding xing de jue ce shu gui na. Ke xue chu ban she, 2012.

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Book chapters on the topic "Fuzzy decision tree"

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Kim, Myung Won, and Joung Woo Ryu. "Optimized Fuzzy Decision Tree Using Genetic Algorithm." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893295_88.

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Bujnowski, Paweł, Eulalia Szmidt, and Janusz Kacprzyk. "Intuitionistic Fuzzy Decision Tree: A New Classifier." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11313-5_68.

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Olaru, Cristina, and Louis Wehenkel. "On neurofuzzy and fuzzy decision tree approaches." In Information, Uncertainty and Fusion. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5209-3_10.

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Intan, Rolly, and Oviliani Yenty Yuliana. "Fuzzy Decision Tree Induction Approach for Mining Fuzzy Association Rules." In Neural Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10684-2_80.

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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. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67137-6_16.

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Wahiba, Ben Abdessalam, and Ben El Fadhl Ahmed. "New Fuzzy Decision Tree Model for Text Classification." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26690-9_28.

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Liu, Zaiqiang, and Dengguo Feng. "Incremental Fuzzy Decision Tree-Based Network Forensic System." In Computational Intelligence and Security. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596981_148.

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Li, Peng, and Seiji Yamada. "Automatic Creation of Links: An Approach Based on Decision Tree." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_158.

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Quang, Vũ Minh, Eric Castelli, and Phạm Ngọc Yên. "A Decision Tree-Based Method for Speech Processing: Question Sentence Detection." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_150.

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Ribeiro, Mariana V., Luiz Manoel S. Cunha, Heloisa A. Camargo, and Luiz Henrique A. Rodrigues. "Applying a Fuzzy Decision Tree Approach to Soil Classification." In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08795-5_10.

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Conference papers on the topic "Fuzzy decision tree"

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Marsala, Christophe, and Maria Rifqi. "Fuzzy decision tree and fuzzy gradual decision tree: Application to job satisfaction." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015740.

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Meng, Qing-wu, Qiang He, Ning Li, Xiang-ran Du, and Li-na Su. "Crisp Decision Tree Induction Based on Fuzzy Decision Tree Algorithm." In 2009 First International Conference on Information Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.440.

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Levashenko, Vitaly, Elena Zaitseva, and Seppo Puuronen. "Fuzzy Classifier Based on Fuzzy Decision Tree." In EUROCON 2007 - The International Conference on "Computer as a Tool". IEEE, 2007. http://dx.doi.org/10.1109/eurcon.2007.4400614.

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Crockett, K., Z. Bandar, and J. O'Shea. "On Producing Balanced Fuzzy Decision Tree Classifiers." In 2006 IEEE International Conference on Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/fuzzy.2006.1681943.

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Wei, Jin-Mao, Shu-Qin Wang, Jun-Ping You, and Guo-Ying Wang. "RST in Decision Tree Pruning." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.502.

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Marsala, Christophe. "A fuzzy decision tree based approach to characterize medical data." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277106.

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Koen-Myung Lee, Kyung-Mi Lee, Jee-Hyong Lee, and Hyung Lee-Kwang. "A fuzzy decision tree induction method for fuzzy data." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.793199.

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Shinkai, Kimiaki, Hajime Yamashitar, and Shuya Kanagawa. "Decision Analysis of Fuzzy Partition Tree Applying Fuzzy Theory." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.236.

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Zhai, Jun-Hai, Shao-Xing Hou, and Su-Fang Zhang. "Induction of tolerance rough fuzzy decision tree." In 2015 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2015. http://dx.doi.org/10.1109/icmlc.2015.7340663.

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Zhang, Haitang, and Hongze Qiu. "Sensitivity Degree Based Fuzzy SLIQ Decision Tree." In 2010 2nd International Conference on Information Engineering and Computer Science (ICIECS). IEEE, 2010. http://dx.doi.org/10.1109/iciecs.2010.5678341.

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