Relevant bibliographies by topics / DECISION TREE TECHNIQUE

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'DECISION TREE TECHNIQUE'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "DECISION TREE TECHNIQUE"

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Dr. S.Vijayarani, Dr S. Vijayarani, and M. Sangeetha M. Sangeetha. "An Efficient Technique for Privacy Preserving Decision Tree Learning." Indian Journal of Applied Research 3, no. 9 (October 1, 2011): 127–30. http://dx.doi.org/10.15373/2249555x/sept2013/40.

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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 (June 11, 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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Maazouzi, Faiz, and Halima Bahi. "Using multi decision tree technique to improving decision tree classifier." International Journal of Business Intelligence and Data Mining 7, no. 4 (2012): 274. http://dx.doi.org/10.1504/ijbidm.2012.051712.

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Kaur, Amanpreet. "IMAGE COMPRESSION USING DECISION TREE TECHNIQUE." International Journal of Advanced Research in Computer Science 8, no. 8 (August 30, 2017): 682–88. http://dx.doi.org/10.26483/ijarcs.v8i8.4812.

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Olaru, Cristina, and Louis Wehenkel. "A complete fuzzy decision tree technique." Fuzzy Sets and Systems 138, no. 2 (September 2003): 221–54. http://dx.doi.org/10.1016/s0165-0114(03)00089-7.

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Sharma, Dr Nirmla, and Sameera Iqbal Muhmmad Iqbal. "Applying Decision Tree Algorithm Classification and Regression Tree (CART) Algorithm to Gini Techniques Binary Splits." International Journal of Engineering and Advanced Technology 12, no. 5 (June 30, 2023): 77–81. http://dx.doi.org/10.35940/ijeat.e4195.0612523.

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Decision tree study is a predictive modelling tool that is used over many grounds. It is constructed through an algorithmic technique that is divided the dataset in different methods created on varied conditions. Decisions trees are the extreme dominant algorithms that drop under the set of supervised algorithms. However, Decision Trees appearance modest and natural, there is nothing identical modest near how the algorithm drives nearby the procedure determining on splits and how tree snipping happens. The initial object to appreciate in Decision Trees is that it splits the analyst field, i.e., the objective parameter into diverse subsets which are comparatively more similar from the viewpoint of the objective parameter. Gini index is the name of the level task that has applied to assess the binary changes in the dataset and worked with the definite object variable “Success” or “Failure”. Split creation is basically covering the dataset values. Decision trees monitor a top-down, greedy method that has recognized as recursive binary splitting. It has statistics for 15 statistics facts of scholar statistics on pass or fails an online Machine Learning exam. Decision trees are in the class of supervised machine learning. It has been commonly applied as it has informal implement, interpreted certainly, derived to quantitative, qualitative, nonstop, and binary splits, and provided consistent outcomes. The CART tree has regression technique applied to expected standards of nonstop variables. CART regression trees are an actual informal technique of understanding outcomes.
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Amraee, Turaj, and Soheil Ranjbar. "Transient Instability Prediction Using Decision Tree Technique." IEEE Transactions on Power Systems 28, no. 3 (August 2013): 3028–37. http://dx.doi.org/10.1109/tpwrs.2013.2238684.

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Bavirthi, Swathi Sowmya, and Supreethi K. P. "Systematic Review of Indexing Spatial Skyline Queries for Decision Support." International Journal of Decision Support System Technology 14, no. 1 (January 2022): 1–15. http://dx.doi.org/10.4018/ijdsst.286685.

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Residing in the data age, researchers inferred that huge amount of geo-tagged data is available and identified the importance of Spatial Skyline queries. Spatial or geographic location in conjunction with textual relevance plays a key role in searching Point of Interest (POI) of the user. Efficient indexing techniques like R-Tree, Quad Tree, Z-order curve and variants of these trees are widely available in terms of spatial context. Inverted file is the popular indexing technique for textual data. As Spatial skyline query aims at analyzing both spatial and skyline dominance, there is a necessity for a hybrid indexing technique. This article presents the review of spatial skyline queries evaluation that include a range of indexing techniques which concentrates on disk access, I/O time, CPU time. The investigation and analysis of studies related to skyline queries based upon the indexing model and research gaps are presented in this review.
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Cho, Sung-bin. "Corporate Bankruptcy Prediction using Decision Tree Ensemble Technique." Journal of the Korea Management Engineers Society 25, no. 4 (December 31, 2020): 63–71. http://dx.doi.org/10.35373/kmes.25.4.5.

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Divyashree, S., and H. R. Divakar. "Prediction of Human Health using Decision Tree Technique." International Journal of Computer Sciences and Engineering 6, no. 6 (June 30, 2018): 805–8. http://dx.doi.org/10.26438/ijcse/v6i6.805808.

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Dissertations / Theses on the topic "DECISION TREE TECHNIQUE"

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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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Li, Yunjie. "Applying Data Mining Techniques on Continuous Sensed Data : For daily living activity recognition." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-23424.

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Nowadays, with the rapid development of the Internet of Things, the applicationfield of wearable sensors has been continuously expanded and extended, especiallyin the areas of remote electronic medical treatment, smart homes ect. Human dailyactivities recognition based on the sensing data is one of the challenges. With avariety of data mining techniques, the activities can be automatically recognized. Butdue to the diversity and the complexity of the sensor data, not every kind of datamining technique can performed very easily, until after a systematic analysis andimprovement. In this thesis, several data mining techniques were involved in theanalysis of a continuous sensing dataset in order to achieve the objective of humandaily activities recognition. This work studied several data mining techniques andfocuses on three of them; Decision Tree, Naive Bayes and neural network, analyzedand compared these techniques according to the classification results. The paper alsoproposed some improvements to the data mining techniques according to thespecific dataset. The comparison of the three classification results showed that eachclassifier has its own limitations and advantages. The proposed idea of combing theDecision Tree model with the neural network model significantly increased theclassification accuracy in this experiment.
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Thomas, Clifford S. "From 'tree' based Bayesian networks to mutual information classifiers : deriving a singly connected network classifier using an information theory based technique." Thesis, University of Stirling, 2005. http://hdl.handle.net/1893/2623.

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For reasoning under uncertainty the Bayesian network has become the representation of choice. However, except where models are considered 'simple' the task of construction and inference are provably NP-hard. For modelling larger 'real' world problems this computational complexity has been addressed by methods that approximate the model. The Naive Bayes classifier, which has strong assumptions of independence among features, is a common approach, whilst the class of trees is another less extreme example. In this thesis we propose the use of an information theory based technique as a mechanism for inference in Singly Connected Networks. We call this a Mutual Information Measure classifier, as it corresponds to the restricted class of trees built from mutual information. We show that the new approach provides for both an efficient and localised method of classification, with performance accuracies comparable with the less restricted general Bayesian networks. To improve the performance of the classifier, we additionally investigate the possibility of expanding the class Markov blanket by use of a Wrapper approach and further show that the performance can be improved by focusing on the class Markov blanket and that the improvement is not at the expense of increased complexity. Finally, the two methods are applied to the task of diagnosing the 'real' world medical domain, Acute Abdominal Pain. Known to be both a different and challenging domain to classify, the objective was to investigate the optiniality claims, in respect of the Naive Bayes classifier, that some researchers have argued, for classifying in this domain. Despite some loss of representation capabilities we show that the Mutual Information Measure classifier can be effectively applied to the domain and also provides a recognisable qualitative structure without violating 'real' world assertions. In respect of its 'selective' variant we further show that the improvement achieves a comparable predictive accuracy to the Naive Bayes classifier and that the Naive Bayes classifier's 'overall' performance is largely due the contribution of the majority group Non-Specific Abdominal Pain, a group of exclusion.
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Dalkiran, Evrim. "Discrete and Continuous Nonconvex Optimization: Decision Trees, Valid Inequalities, and Reduced Basis Techniques." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77366.

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This dissertation addresses the modeling and analysis of a strategic risk management problem via a novel decision tree optimization approach, as well as development of enhanced Reformulation-Linearization Technique (RLT)-based linear programming (LP) relaxations for solving nonconvex polynomial programming problems, through the generation of valid inequalities and reduced representations, along with the design and implementation of efficient algorithms. We first conduct a quantitative analysis for a strategic risk management problem that involves allocating certain available failure-mitigating and consequence-alleviating resources to reduce the failure probabilities of system safety components and subsequent losses, respectively, together with selecting optimal strategic decision alternatives, in order to minimize the risk or expected loss in the event of a hazardous occurrence. Using a novel decision tree optimization approach to represent the cascading sequences of probabilistic events as controlled by key decisions and investment alternatives, the problem is modeled as a nonconvex mixed-integer 0-1 factorable program. We develop a specialized branch-and-bound algorithm in which lower bounds are computed via tight linear relaxations of the original problem that are constructed by utilizing a polyhedral outer-approximation mechanism in concert with two alternative linearization schemes having different levels of tightness and complexity. We also suggest three alternative branching schemes, each of which is proven to guarantee convergence to a global optimum for the underlying problem. Extensive computational results and sensitivity analyses are presented to provide insights and to demonstrate the efficacy of the proposed algorithm. In particular, our methodology outperformed the commercial software BARON (Version 8.1.5), yielding a more robust performance along with an 89.9% savings in effort on average. Next, we enhance RLT-based LP relaxations for polynomial programming problems by developing two classes of valid inequalities: v-semidefinite cuts and bound-grid-factor constraints. The first of these uses concepts derived from semidefinite programming. Given an RLT relaxation, we impose positive semidefiniteness on suitable dyadic variable-product matrices, and correspondingly derive implied semidefinite cuts. In the case of polynomial programs, there are several possible variants for selecting such dyadic variable-product matrices for imposing positive semidefiniteness restrictions in order to derive implied valid inequalities, which leads to a new class of cutting planes that we call v-semidefinite cuts. We explore various strategies for generating such cuts within the context of an RLT-based branch-and-cut scheme, and exhibit their relative effectiveness towards tightening the RLT relaxations and solving the underlying polynomial programming problems, using a test-bed of randomly generated instances as well as standard problems from the literature. Our results demonstrate that these cutting planes achieve a significant tightening of the lower bound in contrast with using RLT as a stand-alone approach, thereby enabling an appreciable reduction in the overall computational effort, even in comparison with the commercial software BARON. Empirically, our proposed cut-enhanced algorithm reduced the computational effort required by the latter two approaches by 44% and 77%, respectively, over a test-bed of 60 polynomial programming problems. As a second cutting plane strategy, we introduce a new class of bound-grid-factor constraints that can be judiciously used to augment the basic RLT relaxations in order to improve the quality of lower bounds and enhance the performance of global branch-and-bound algorithms. Certain theoretical properties are established that shed light on the effect of these valid inequalities in driving the discrepancies between RLT variables and their associated nonlinear products to zero. To preserve computational expediency while promoting efficiency, we propose certain concurrent and sequential cut generation routines and various grid-factor selection rules. The results indicate a significant tightening of lower bounds, which yields an overall reduction in computational effort of 21% for solving a test-bed of 15 challenging polynomial programming problems to global optimality in comparison with the basic RLT procedure, and over a 100-fold speed-up in comparison with the commercial software BARON. Finally, we explore equivalent, reduced size RLT-based formulations for polynomial programming problems. Utilizing a basis partitioning scheme for an embedded linear equality subsystem, we show that a strict subset of RLT defining equalities imply the remaining ones. Applying this result, we derive significantly reduced RLT representations and develop certain coherent associated branching rules that assure convergence to a global optimum, along with static as well as dynamic basis selection strategies to implement the proposed procedure. In addition, we enhance the RLT relaxations with v-semidefinite cuts, which are empirically shown to further improve the relative performance of the reduced RLT method over the usual RLT approach. Computational results presented using a test-bed of 10 challenging polynomial programs to evaluate the different reduction strategies demonstrate that our superlative proposed approach achieved more than a four-fold improvement in computational effort in comparison with both the commercial software BARON and a recently developed open-source code, Couenne, for solving nonconvex mixed-integer nonlinear programming problems. Moreover, our approach robustly solved all the test cases to global optimality, whereas BARON and Couenne were jointly able to solve only a single instance to optimality within the set computational time limit, having an unresolved average optimality gap of 260% and 437%, respectively, for the other nine instances. This dissertation makes several broader contributions to the field of nonconvex optimization, including factorable, nonlinear mixed-integer programming problems. The proposed decision tree optimization framework can serve as a versatile management tool in the arenas of homeland security and health-care. Furthermore, we have advanced the frontier for tackling formidable nonconvex polynomial programming problems that arise in emerging fields such as signal processing, biomedical engineering, materials science, and risk management. An open-source software using the proposed reduced RLT representations, semidefinite cuts, bound-grid-factor constraints, and range reduction strategies, is currently under preparation. In addition, the different classes of challenging polynomial programming test problems that are utilized in the computational studies conducted in this dissertation have been made available for other researchers via the Web-page http://filebox.vt.edu/users/dalkiran/website/. It is our hope and belief that the modeling and methodological contributions made in this dissertation will serve society in a broader context through the myriad of widespread applications they support.
Ph. D.
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Twala, Bhekisipho. "Effective techniques for handling incomplete data using decision trees." Thesis, Open University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418465.

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Millerand, Gaëtan. "Enhancing decision tree accuracy and compactness with improved categorical split and sampling techniques." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279454.

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Decision tree is one of the most popular algorithms in the domain of explainable AI. From its structure, it is simple to induce a set of decision rules which are totally understandable for a human. That is why there is currently research on improving decision or mapping other models into a tree. Decision trees generated by C4.5 or ID3 tree suffer from two main issues. The first one is that they often have lower performances in term of accuracy for classification tasks or mean square error for regression tasks compared to state-of-the-art models like XGBoost or deep neural networks. On almost every task, there is an important gap between top models like XGboost and decision trees. This thesis addresses this problem by providing a new method based on data augmentation using state-of-the-art models which outperforms the old ones regarding evaluation metrics. The second problem is the compactness of the decision tree, as the depth increases the set of rules becomes exponentially big, especially when the splitted attribute is a categorical one. Standards solution to handle categorical values are to turn them into dummy variables or to split on each value producing complex models. A comparative study of current methods of splitting categorical values in classification problems is done in this thesis. A new method is also studied in the case of regression.
Beslutsträd är en av de mest populära algoritmerna i den förklarbara AI-domänen. I själva verket är det från dess struktur verkligen enkelt att framställa en uppsättning beslutsregler som är helt förståeliga för en vanlig användare. Därför forskas det för närvarande på att förbättra beslut eller kartlägga andra modeller i ett träd. Beslutsträd genererat av C4.5 eller ID3-träd lider av två huvudproblem. Den första är att de ofta har lägre prestanda när det gäller noggrannhet för klassificeringsuppgifter eller medelkvadratfel för regressionsuppgiftens noggrannhet jämfört med modernaste modeller som XGBoost eller djupa neurala nätverk. I nästan varje uppgift finns det faktiskt ett viktigt gap mellan toppmodeller som XGboost och beslutsträd. Detta examensarbete tar upp detta problem genom att tillhandahålla en ny metod baserad på dataförstärkning med hjälp av modernaste modeller som överträffar de gamla när det gäller utvärderingsmätningar. Det andra problemet är beslutsträdets kompakthet, allteftersom djupet ökar, blir uppsättningen av regler exponentiellt stor, särskilt när det delade attributet är kategoriskt. Standardlösning för att hantera kategoriska värden är att förvandla dem till dummiesvariabler eller dela på varje värde som producerar komplexa modeller. En jämförande studie av nuvarande metoder för att dela kategoriska värden i klassificeringsproblem görs i detta examensarbete, en ny metod studeras också i fallet med regression.
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Valente, Lorenzo. "Reconstruction of non-prompt charmed baryon Λc with boosted decision trees technique." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21033/.

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L'esperimento ALICE studia la fisica dell'interazione forte a estreme densità di energia attraverso la collisione di ioni pesanti. In tali condizioni è possibile la formazione dello stato della materia chiamato plasma di quark e gluoni. A causa della ridotta vita media di tale stato, lo studio è molto complesso ed è pertanto possibile condurlo solo in modo indiretto sulla base delle modalità di raffreddamento e dalle particelle rilasciate nel processo. Uno dei principali metodi d’indagine è lo studio di adroni contenenti quark pesanti (charm e beauty) e di come queste particelle, prodotte nei primi stadi della collisione, interagiscono con questo stato della materia. L'obiettivo della tesi è la ricostruzione del barione charmato Λc e la distinzione del segnale non-prompt da quello prompt attraverso la tecnica dei Boosted Decision Trees. L'analisi è stata condotta attraverso l'approccio di analisi multivariata in cui è possibile considerare le proprietà di più eventi contemporaneamente, ricavando il maggior numero di informazioni attraverso tecniche di machine learning.
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Townsend, Whitney Jeanne. "Discrete function representations utilizing decision diagrams and spectral techniques." Thesis, Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-07012002-160303.

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Ravula, Ravindar Reddy. "Classification of Malware using Reverse Engineering and Data Mining Techniques." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1311042709.

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Jia, Xiuping Electrical Engineering Australian Defence Force Academy UNSW. "Classification techniques for hyperspectral remote sensing image data." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Electrical Engineering, 1996. http://handle.unsw.edu.au/1959.4/38713.

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Hyperspectral remote sensing image data, such as that recorded by AVIRIS with 224 spectral bands, provides rich information on ground cover types. However, it presents new problems in machine assisted interpretation, mainly in long processing times and the difficulties of class training due to the low ratio of number of training samples to the number of bands. This thesis investigates feasible and efficient feature reduction and image classification techniques which are appropriate for hyperspectral image data. The study is reported in three parts. The first concerns a deterministic approach for hyperspectral data interpretation. Multigroup and multiple threshold spectral coding procedures, and associated techniques for spectral matching and classification, are proposed and tested. By coding on subgroups of bands using one or three thresholds, spectral searching and matching becomes simple, fast and free of the need for radiometric correction. Modifications of existing statistical techniques are proposed in the second part of the investigation A block-based maximum likelihood classification technique is developed. Several subgroups are formed from the complete set of spectral bands in the data, based on the properties of global correlation among the bands. Subgroups which are poorly correlated with each other are treated independently using conventional maximum likelihood classification. Experimental results demonstrate that, when using appropriate subgroup sizes, the new method provides a compromise among classification accuracy, processing time and available training pixels. Furthermore, a segmented, and possibly multi-layer, principal components transformation is proposed as a possible feature reduction technique prior to classification, and for effective colour display. The transformation is performed efficiently on each of the highly correlated subgroups of bands independently. Selected features from each transformed subgroup can be then transformed again to achieve a satisfactory data reduction ratio and to generate the three most significant components for colour display. Classification accuracy is improved and high quality colour image display is achieved in experiments using two AVIRIS data sets.
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Books on the topic "DECISION TREE TECHNIQUE"

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Irniger, Christophe-André Mario. Graph matching: Filtering databases of graphs using machine learning techniques. Berlin: AKA, 2005.

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Vidales, A. MACHINE LEARNING with MATLAB. CLASSIFICATION TECHNIQUES: CLUSTER ANALYSIS, DECISION TREES, DISCRIMINANT ANALYSIS and NAIVE BAYES. Independently Published, 2019.

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Criminisi, A., J. Shotton, and Antonio Criminisi. Decision Forests for Computer Vision and Medical Image Analysis. Springer London, Limited, 2016.

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Decision Forests For Computer Vision And Medical Image Analysis. Springer London Ltd, 2013.

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López, César Pérez. DATA MINING and MACHINE LEARNING. PREDICTIVE TECHNIQUES : ENSEMBLE METHODS, BOOSTING, BAGGING, RANDOM FOREST, DECISION TREES and REGRESSION TREES.: Examples with MATLAB. Lulu Press, Inc., 2021.

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Chastre, Jean. Diagnosis and management of nosocomial pneumonia. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0117.

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Quantitative culture techniques, performed before the introduction of new antibiotics, enable physicians to identify most patients who need immediate treatment for nosocomial pneumonia, and help select optimal therapy in a safe, well-tolerated manner. These techniques avoid resorting to broad-spectrum coverage of all patients with a clinical suspicion of infection, and may minimize the emergence of resistant micro-organisms in the intensive care unit. However, the full impact of this decision tree on patient outcome remains controversial. Antimicrobial therapy of patients with nosocomial pneumonia is a two-stage process. The first stage involves administering broad-spectrum antibiotics at doses maximizing bacterial killing as soon as possible to avoid inadequate treatment in patients with true bacterial pneumonia. The second stage focuses on trying to achieve this objective without overusing or abusing antibiotics. This will need the combination of a number of different steps, including commitment to focused and narrow treatment once the aetiological agents are known, switching to monotherapy after day 3, and shortening duration of therapy to 7–8 days in most patients, as dictated by the patient’s clinical response and microbiological information.
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DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES : SUPPORT VECTOR MACHINE, LOGISTIC REGRESSION, DISCRIMINANT ANALYSIS and DECISION TREES: Examples with MATLAB. Lulu Press, Inc., 2021.

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Kerrigan, John. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198793755.003.0001.

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Most people interested in Shakespeare have wondered about his originality. Is it true that his plays were adapted from other authors’ plays, poems, and romances? Are his best-known speeches really lifted out of Montaigne and Plutarch? If so—and it is far from entirely so—does it matter, any more than it does when a classic movie is based on a novel? What distinctions and relationships hold between originality, collaboration, and adaptation? To think adequately about such questions requires a lot of information-gathering and sifting, but the effort is worthwhile because it helps us identify creative decisions made by Shakespeare in the process of composition while it also shows him participating in a larger culture of play-making. We equip ourselves to characterize the techniques by which he managed to achieve the types of originality available during his lifetime....
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Mooney, Raymond J. Machine Learning. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0020.

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This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described by a set of features. These techniques can be applied to learn knowledge required for a variety of problems in computational linguistics ranging from part-of-speech tagging and syntactic parsing to word-sense disambiguation and anaphora resolution. Finally, this article reviews the applications to a variety of these problems, such as morphology, part-of-speech tagging, word-sense disambiguation, syntactic parsing, semantic parsing, information extraction, and anaphora resolution.
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Craig, Anne, and Anthea Hatfield. The Complete Recovery Room Book. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198846840.001.0001.

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New technologies are increasingly available for patient care but simple ‘tried and true’ old fashioned methods are still essential. The care that a patient receives in the first hours after surgery is crucial to minimizing the risk of complications such as heart attacks, pneumonia, and blood clots. As the patient awakes from their drug-induced coma, it takes time for them to metabolize and excrete drugs. They remain unable to care for themselves, and are at increased risk of harm. The recovery room staff must manage both comatose and physiologically unstable patients and deal with the immediate postoperative care of surgical patients. The sixth edition of this popular book, introducing a new author Dr Anne Craig, will provide nurses, surgeons and anaesthetists guidance on how to manage day-to-day problems and make difficult decisions. Previous editions of this book have established it as the definitive guide to setting-up, equipping, staffing, and administering an acute care unit. Basic science, physiology and pharmacology are fully explained. There are chapters on specific symptoms including pain and vomiting, and chapters devoted to the unique postoperative needs of individual types of surgery. This new edition brings this important text up to date and new drugs and techniques for monitoring are described. A new section looks ahead to the future development and design of recovery rooms and how they can contribute to patient well-being.
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Book chapters on the topic "DECISION TREE TECHNIQUE"

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Xiang, Yu, and Li Ma. "A Priority Heuristic Correlation Technique for Decision Tree Pruning." In Lecture Notes in Electrical Engineering, 176–82. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9244-4_25.

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Tinabo, Rose. "Decision Tree Technique for Customer Retention in Retail Sector." In Communications in Computer and Information Science, 123–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22247-4_11.

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Desai, Vijaya S., and Sharad Joshi. "Application of Decision Tree Technique to Analyze Construction Project Data." In Information Systems, Technology and Management, 304–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12035-0_30.

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Ameer Basha, G., K. Lakshmana Gupta, and K. Ramakrishna. "Expectation of Radar Returns from Ionosphere Using Decision Tree Technique." In Advances in Data Science and Management, 209–14. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0978-0_20.

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Nilosey, Shivam, Abhishek Pipliya, and Vijay Malviya. "Real-Time Classification of Twitter Data Using Decision Tree Technique." In Social Networking and Computational Intelligence, 173–81. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2071-6_14.

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Abdelhalim, Amany, Issa Traore, and Bassam Sayed. "RBDT-1: A New Rule-Based Decision Tree Generation Technique." In Lecture Notes in Computer Science, 108–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04985-9_12.

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Schetinin, Vitaly, Jonathan E. Fieldsend, Derek Partridge, Wojtek J. Krzanowski, Richard M. Everson, Trevor C. Bailey, and Adolfo Hernandez. "Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data." In Perception-based Data Mining and Decision Making in Economics and Finance, 155–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-36247-0_6.

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Walia, Himdweep, Ajay Rana, and Vineet Kansal. "A Decision Tree Based Supervised Program Interpretation Technique for Gurmukhi Language." In Data Science and Analytics, 356–65. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5830-6_30.

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Settu, Nithya, and M. Rajasekhara Babu. "Enhancing the Performance of Decision Tree Using NSUM Technique for Diabetes Patients." In Internet of Things and Personalized Healthcare Systems, 13–20. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0866-6_2.

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Ahlawat, Khyati, and Amit Prakash Singh. "A Novel Hybrid Technique for Big Data Classification Using Decision Tree Learning." In Communications in Computer and Information Science, 118–28. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6427-2_10.

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Conference papers on the topic "DECISION TREE TECHNIQUE"

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Zukhronah, Etik, Yuliana Susanti, Hasih Pratiwi, Respatiwulan, and Sri Sulistijowati H. "Decision tree technique for classifying cassava production." In THE 8TH ANNUAL BASIC SCIENCE INTERNATIONAL CONFERENCE: Coverage of Basic Sciences toward the World’s Sustainability Challanges. Author(s), 2018. http://dx.doi.org/10.1063/1.5062777.

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Cheng, Ken Chau-Cheung, Katherine Shu-Min Li, Sying-Jyan Wang, Andrew Yi-Ann Huang, Chen-Shiun Lee, Leon Li-Yang Chen, Peter Yi-Yu Liao, and Nova Cheng-Yen Tsai. "Wafer Defect Pattern Classification with Explainable-Decision Tree Technique." In 2022 IEEE International Test Conference (ITC). IEEE, 2022. http://dx.doi.org/10.1109/itc50671.2022.00070.

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Zakerian, A., A. Maleki, Y. Mohammadnian, and T. Amraee. "Bad data detection in state estimation using Decision Tree technique." In 2017 Iranian Conference on Electrical Engineering (ICEE). IEEE, 2017. http://dx.doi.org/10.1109/iraniancee.2017.7985192.

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Schetinin, Vitaly, Wojtek Krzanowski, and Carsten Maple. "The Bayesian Decision Tree Technique Using an Adaptive Sampling Scheme." In Twentieth IEEE International Symposium on Computer-Based Medical Systems. IEEE, 2007. http://dx.doi.org/10.1109/cbms.2007.109.

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Bhat, Ishani, V. Umadevi, Nishchitha Jagadeesh, Savithri Bhat, and Rashmi S. Shenoy. "Tender Coconut Classification using Decision Tree and Deep Learning Technique." In 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2023. http://dx.doi.org/10.1109/spin57001.2023.10117353.

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Keerthika, J., D. Sruthi, D. Swathi, S. Swetha, and R. Vinupriya. "Diagnosis of Breast Cancer using Decision Tree Data Mining Technique." In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2021. http://dx.doi.org/10.1109/icaccs51430.2021.9442043.

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Zen, Heiga, Keiichi Tokuda, and Tadashi Kitamura. "Decision tree distribution tying based on a dimensional split technique." In 7th International Conference on Spoken Language Processing (ICSLP 2002). ISCA: ISCA, 2002. http://dx.doi.org/10.21437/icslp.2002-387.

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Gupta, Varun, Neeraj Garg, and Tarun Gupta. "Search Bot: Search Intention Based Filtering Using Decision Tree Based Technique." In 2012 3rd International Conference on Intelligent Systems, Modelling and Simulation (ISMS). IEEE, 2012. http://dx.doi.org/10.1109/isms.2012.78.

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Das, Ariyam, Jin Wang, Sahil M. Gandhi, Jae Lee, Wei Wang, and Carlo Zaniolo. "Learn Smart with Less: Building Better Online Decision Trees with Fewer Training Examples." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/306.

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Online decision tree models are extensively used in many industrial machine learning applications for real-time classification tasks. These models are highly accurate, scalable and easy to use in practice. The Very Fast Decision Tree (VFDT) is the classic online decision tree induction model that has been widely adopted due to its theoretical guarantees as well as competitive performance. However, VFDT and its variants solely rely on conservative statistical measures like Hoeffding bound to incrementally grow the tree. This makes these models extremely circumspect and limits their ability to learn fast. In this paper, we efficiently employ statistical resampling techniques to build an online tree faster using fewer examples. We first theoretically show that a naive implementation of resampling techniques like non-parametric bootstrap does not scale due to large memory and computational overheads. We mitigate this by proposing a robust memory-efficient bootstrap simulation heuristic (Mem-ES) that successfully expedites the learning process. Experimental results on both synthetic data and large-scale real world datasets demonstrate the efficiency and effectiveness of our proposed technique.
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Derrouiche, Ridha, Pongsak Holimchayachotikul, and Komgrit Leksakul. "Predictive performance model in collaborative supply chain using decision tree and clustering technique." In 2011 4th International Conference on Logistics (LOGISTIQUA). IEEE, 2011. http://dx.doi.org/10.1109/logistiqua.2011.5939435.

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Reports on the topic "DECISION TREE TECHNIQUE"

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Quiller, Ryan. Decision Tree Technique for Particle Identification. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/815649.

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Zio, Enrico, and Nicola Pedroni. Uncertainty characterization in risk analysis for decision-making practice. Fondation pour une culture de sécurité industrielle, May 2012. http://dx.doi.org/10.57071/155chr.

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This document provides an overview of sources of uncertainty in probabilistic risk analysis. For each phase of the risk analysis process (system modeling, hazard identification, estimation of the probability and consequences of accident sequences, risk evaluation), the authors describe and classify the types of uncertainty that can arise. The document provides: a description of the risk assessment process, as used in hazardous industries such as nuclear power and offshore oil and gas extraction; a classification of sources of uncertainty (both epistemic and aleatory) and a description of techniques for uncertainty representation; a description of the different steps involved in a Probabilistic Risk Assessment (PRA) or Quantitative Risk Assessment (QRA), and an analysis of the types of uncertainty that can affect each of these steps; annexes giving an overview of a number of tools used during probabilistic risk assessment, including the HAZID technique, fault trees and event tree analysis.
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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41182.

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