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Relevant bibliographies by topics / Decision table and tree

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Academic literature on the topic 'Decision table and tree'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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Journal articles on the topic "Decision table and tree"

1

Kim, Taehwan, and Taeseon Yoon. "Artificial Neural Network Hybrid Algorithm Combimed with Decision Tree and Table." International Journal of Machine Learning and Computing 5, no. 6 (2015): 471–75. http://dx.doi.org/10.18178/ijmlc.2015.5.6.555.

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Subramanian, Girish H., John Nosek, Sankaran P. Raghunathan, and Santosh S. Kanitkar. "A comparison of the decision table and tree." Communications of the ACM 35, no. 1 (1992): 89–94. http://dx.doi.org/10.1145/129617.129621.

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ZANTEMA, HANS, and HANS L. BODLAENDER. "SIZES OF ORDERED DECISION TREES." International Journal of Foundations of Computer Science 13, no. 03 (2002): 445–58. http://dx.doi.org/10.1142/s0129054102001205.

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Decision tables provide a natural framework for knowledge acquisition and representation in the area of knowledge based information systems. Decision trees provide a standard method for inductive inference in the area of machine learning. In this paper we show how decision tables can be considered as ordered decision trees: decision trees satisfying an ordering restriction on the nodes. Every decision tree can be represented by an equivalent ordered decision tree, but we show that doing so may exponentially blow up sizes, even if the choice of the order is left free. Our main result states tha

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许, 美玲. "Decision Tree Analysis for Inconsistent Decision Tables." Computer Science and Application 06, no. 10 (2016): 597–606. http://dx.doi.org/10.12677/csa.2016.610074.

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Azad, Mohammad, and Mikhail Moshkov. "Minimization of Decision Tree Average Depth for Decision Tables with Many-valued Decisions." Procedia Computer Science 35 (2014): 368–77. http://dx.doi.org/10.1016/j.procs.2014.08.117.

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Qureshi, M. Shuaib. "Proposed architectural model for optimal transformation of decision table and decision tree into knowledge base." Indian Journal of Science and Technology 3, no. 3 (2010): 362–65. http://dx.doi.org/10.17485/ijst/2010/v3i3.31.

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乔, 莹. "Minimal Decision Tree Generation for Multi-Label Decision Tables." Computer Science and Application 06, no. 10 (2016): 617–28. http://dx.doi.org/10.12677/csa.2016.610076.

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Wibowo, Agung, Rival Afrian, Saeful Bahri, Taufik Hidayatulloh, and Rusda Wajhillah. "SISTEM PAKAR HUKUM PIDANA PENCURIAN MENGGUNAKAN ALGORITMA FUZZY DECISION TABLE BERBASIS ANDROID." InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) 2, no. 2 (2018): 130–34. http://dx.doi.org/10.30743/infotekjar.v2i2.267.

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Hukum pidana yang komplek sulit dimengerti orang awam dalam memilah pasal-pasal yang mengaturnya, untuk lebih mempermudah masyrakat dalam mengerti dan memilah tentang pasal-pasal yang digunakan untuk menjerat pelaku hukum pidana, pada peneitian ini akan encoba menerapkan sebuah algoritma yang mengelompokan dan menentukan kadar dari sebuah tinggkat kejahatan yang dilakukan dalam hal ini algoritma yang kami gunakan yaitu fuzzy decision tree yang telah terbukti cocok untuk kasus yang yang mempunyai nilai abu-abu,algoritma ini kami gunakan untuk untuk menentukan perundangan mana yang yang cocokdis

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Mihalciuc, Camelia Cătălina, and Grosu Maria. "INSTRUMENTS SPECIFIC TO THE PROCESS OF SUBSTANTIATING MANAGERIAL DECISIONS AT THE ORGANIZATION’S LEVEL." Balkans Journal of Emerging Trends in Social Sciences 2, no. 2 (2019): 194–209. http://dx.doi.org/10.31410/balkans.jetss.2019.2.2.194-209.

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The main objective of this paper is first to analyze those instruments that have proven to be useful in time and have the merit of being able to be used in the decision-making processes of all types of organizations such as the tree decision-making or decision table and on the other hand, another important aspect is the presentation of the organization’s integrated piloting tools such as scoreboard and balanced scorecard, tools that allow managers to dispose, in real time, of a synthetic view of the main indicators of the organization and the business environment for taking decisions under the

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YANG, B., D. LIM, and A. TAN. "VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table." Expert Systems with Applications 28, no. 4 (2005): 735–42. http://dx.doi.org/10.1016/j.eswa.2004.12.030.

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Dissertations / Theses on the topic "Decision table and tree"

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Badr, Bashar. "Implementation of decision trees for embedded systems." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/14711.

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This research work develops real-time incremental learning decision tree solutions suitable for real-time embedded systems by virtue of having both a defined memory requirement and an upper bound on the computation time per training vector. In addition, the work provides embedded systems with the capabilities of rapid processing and training of streamed data problems, and adopts electronic hardware solutions to improve the performance of the developed algorithm. Two novel decision tree approaches, namely the Multi-Dimensional Frequency Table (MDFT) and the Hashed Frequency Table Decision Tree

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Saeed, Umar, and Ansur Mahmood Amjad. "ISTQB : Black Box testing Strategies used in Financial Industry for Functional testing." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3237.

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Black box testing techniques are important to test the functionality of the system without knowing its inner detail which makes sure correct, consistent, complete and accurate behavior or function of a system. Black box testing strategies are used to test logical, data or behavioral dependencies, to generate test data and quality of test cases which have potential to guess more defects. Black box testing strategies play pivotal role to detect possible defects in system and can help in successful completion of system according to functionality. The studies of five companies regarding important

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

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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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Books on the topic "Decision table and tree"

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Alsolami, Fawaz, Mohammad Azad, Igor Chikalov, and Mikhail Moshkov. Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-12854-8.

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Gladwin, Christina. Ethnographic decision tree modeling. Sage, 1989.

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Gladwin, Christina H. Ethnographic decision tree modeling. Sage, 1989.

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Ken, Friedman. The decision tree: A novel. Heart Pub., 1996.

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Euler, Bryan L. EDDT: Emotional Disturbance Decision Tree. Psychological Assessment Resources, 2007.

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Knickerbocker, Peggy. Olive oil: From tree to table. Chronicle Books, 1997.

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Knickerbocker, Peggy. Olive oil: From tree to table. Chronicle Books, 1997.

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Drechsler, Rolf. Binary decision diagrams: Theory and implementation. Kluwer Academic Publishers, 1998.

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Grąbczewski, Krzysztof. Meta-Learning in Decision Tree Induction. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00960-5.

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Association, American Bankers. Analyzing financial statements: A decision tree approach. American Bankers Association, 2013.

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Book chapters on the topic "Decision table and tree"

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Bramer, Max. "Decision Tree Induction: Using Frequency Tables for Attribute Selection." In Principles of Data Mining. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7307-6_6.

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Bramer, Max. "Decision Tree Induction: Using Frequency Tables for Attribute Selection." In Principles of Data Mining. Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-7493-6_6.

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Bramer, Max. "Decision Tree Induction: Using Frequency Tables for Attribute Selection." In Principles of Data Mining. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4884-5_6.

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Azad, Mohammad, Igor Chikalov, and Mikhail Moshkov. "Three Approaches to Deal with Inconsistent Decision Tables - Comparison of Decision Tree Complexity." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41218-9_6.

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Moshkov, Mikhail Ju. "Greedy Algorithm of Decision Tree Construction for Real Data Tables." In Transactions on Rough Sets I. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27794-1_7.

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Alsolami, Fawaz, Mohammad Azad, Igor Chikalov, and Mikhail Moshkov. "Decision and Inhibitory Trees." In Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12854-8_6.

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Moshkov, Mikhail. "Decision Trees for Decision Tables." In Rough Sets, Fuzzy Sets and Knowledge Discovery. Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-3238-7_39.

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Bingham, John, and Garth Davies. "Decision Trees and Tables." In Systems Analysis. Macmillan Education UK, 1992. http://dx.doi.org/10.1007/978-1-349-12833-4_14.

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Alsolami, Fawaz, Mohammad Azad, Igor Chikalov, and Mikhail Moshkov. "Multi-stage Optimization of Decision and Inhibitory Trees." In Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12854-8_7.

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Fürnkranz, Johannes. "Decision Tree." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_66-1.

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Conference papers on the topic "Decision table and tree"

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Sutheebanjard, Phaisarn, and Wichian Premchaiswadi. "Fast convert OR-decision table to decision tree." In Knowledge Engineering 2010). IEEE, 2010. http://dx.doi.org/10.1109/ictke.2010.5692910.

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Li, Zhenqiang, Shengliang Zhang, and Yan Ma. "Decision Tree Based Algorithm for IPv6 Routing Table Lookup." In 2006 International Conference on Communication Technology. IEEE, 2006. http://dx.doi.org/10.1109/icct.2006.341799.

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Shamim, Azra, Hameed Hussain, and Maqbool Uddin Shaikh. "A framework for generation of rules from decision tree and decision table." In 2010 International Conference on Information and Emerging Technologies (ICIET). IEEE, 2010. http://dx.doi.org/10.1109/iciet.2010.5625700.

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Zhang, Baochen. "Tactical Decision System of Table Tennis Match based on C4.5 Decision Tree." In 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2021. http://dx.doi.org/10.1109/icmtma52658.2021.00146.

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Pan, Siwen, Jiehong Wu, Yanan Sun, and Yi Qu. "A Military Chess Game Tree Algorithm Based on Refresh Probability Table." In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164878.

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Wang, J. D., and T. S. Liu. "Extended Decision Tables for Multistate Fault Tree Synthesis." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0057.

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Abstract This study presents component modeling based on extended decision tables for multistate fault tree synthesis. Events are defined by both qualitative and quantitative forms. Compound functions of subsystems, such as redundancy, are described by virtual transfer components. Editing procedures are implemented to expedite the synthesizing process and prune the generated fault trees. A pressure tank system is employed as an example to demonstrate the proposed mothed.

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Azad, Mohammad, and Mikhail Moshkov. "Minimization of decision tree depth for multi-label decision tables." In 2014 IEEE International Conference on Granular Computing (GrC). IEEE, 2014. http://dx.doi.org/10.1109/grc.2014.6982798.

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Leng, Bing, Liusheng Huang, Chunming Qiao, and Hongli Xu. "A decision-tree-based on-line flow table compressing method in Software Defined Networks." In 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS). IEEE, 2016. http://dx.doi.org/10.1109/iwqos.2016.7590401.

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Ouyang, Zhenzheng, Quanyuan Wu, and Tao Wang. "An Efficient Decision Tree Classification Method Based on Extended Hash Table for Data Streams Mining." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.481.

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Rauber, Thomas W., and A. S. Steiger-Garcao. "Decision trees for symbolic knowledge based on contingency table analysis." In Optical Engineering and Photonics in Aerospace Sensing, edited by Firooz A. Sadjadi. SPIE, 1993. http://dx.doi.org/10.1117/12.150599.

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Reports on the topic "Decision table and tree"

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Hamilton, Jill, and Tuan Nguyen. Asbestos Inspection/Reinspection Decision Tree. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada370454.

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Narlikar, Girija J. A Parallel, Multithreaded Decision Tree Builder. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada363531.

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

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Mughal, Mohamed. Biological Weapons Response Template and Decision Tree. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada385897.

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Dakin, Gordon, and Sankar Virdhagriswaran. Misleading Information Detection Through Probabilistic Decision Tree Classifiers. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada406823.

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Kwon, Theresa Hyunjin, Erin Cho, and Youn-Kyung Kim. Identifying Sustainable Style Consumers with Decision Tree Predictive Model. Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1366.

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Eccleston, C. H. The decision - identification tree: A new EIS scoping tool. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/16876.

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Mikulski, Dariusz G. Rough Set Based Splitting Criterion for Binary Decision Tree Classifiers. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada489077.

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Scarpetta, Veda D. Watervliet Arsenal Decision Tree Program for Evaluating Hazardous Spills (SPILL). Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada369834.

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Zaman, Md Mostafa, Theresa Hyunjin Kwon, Katrina Laemmerhirt, and Youn-Kyung Kim. Profiling Second-hand Clothing Shoppers with Decision Tree Predictive Model. Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-407.

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