Готові списки джерел за темами / Tree of decisions

Добірка наукової літератури з теми "Tree of decisions"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Tree of decisions"

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Scott, Jessie, and David Betters. "Economic Analysis of Urban Tree Replacement Decisions." Arboriculture & Urban Forestry 26, no. 2 (March 1, 2000): 69–77. http://dx.doi.org/10.48044/jauf.2000.008.

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Анотація:
Urban forest managers often are required to make decisions about whether to retain or replace an existing tree. In part, this decision relies on an economic analysis of the benefits and costs of the alternatives. This paper presents an economic methodology that helps address the tree replacement problem. The procedures apply to analyzing the benefits and costs of existing trees as well as future replacement trees. A case study, involving a diseased American elm (Uimus americana) is used to illustrate an application of the methodology. The procedures should prove useful in developing economic guides for tree replacement/retention decisions.
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2

TOFAN, Cezarina Adina. "Method of decision tree applied in adopting the decision for promoting a company." Annals of "Spiru Haret". Economic Series 15, no. 3 (September 30, 2015): 47. http://dx.doi.org/10.26458/1535.

Повний текст джерела
Анотація:
The decision can be defined as the way chosen from several possible to achieve an objective. An important role in the functioning of the decisional-informational system is held by the decision-making methods. Decision trees are proving to be very useful tools for taking financial decisions or regarding the numbers, where a large amount of complex information must be considered. They provide an effective structure in which alternative decisions and the implications of their choice can be assessed, and help to form a correct and balanced vision of the risks and rewards that may result from a certain choice. For these reasons, the content of this communication will review a series of decision-making criteria. Also, it will analyse the benefits of using the decision tree method in the decision-making process by providing a numerical example. On this basis, it can be concluded that the procedure may prove useful in making decisions for companies operating on markets where competition intensity is differentiated.
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3

AMPUŁA, Dariusz. "Prediction of Post-Diagnostic Decisions for Tested Hand Grenades’ Fuzes Using Decision Trees." Problems of Mechatronics Armament Aviation Safety Engineering 12, no. 2 (June 30, 2021): 39–54. http://dx.doi.org/10.5604/01.3001.0014.9332.

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Анотація:
The article presents a brief history of creation of decision trees and defines the purpose of the undertaken works. The process of building a classification tree, according to the CHAID method, is shown paying particular attention to the disadvantages, advantages, and characteristics features of this method, as well as to the formal requirements that are necessary to build this model. The tree’s building method for UZRGM (Universal Modernised Fuze of Hand Grenades) fuzes was characterized, specifying the features of the tested hand grenade fuzes and the predictors used that are necessary to create the correct tree model. A classification tree was built basing on the test results, assuming the accepted post-diagnostic decision as a qualitative dependent variable. A schema of the designed tree for the first diagnostic tests, its full structure and the size of individual classes of the node are shown. The matrix of incorrect classifications was determined, which determines the accuracy of incorrect predictions, i.e., correctness of the performed classification. A sheet with risk assessment and standard error for the learning sample and the v-fold cross-check were presented. On the selected examples, the quality of the resulting predictive model was assessed by means of a graph of the cumulative value of the lift coefficient and the "ROC" curve
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4

Jiang, Daniel R., Lina Al-Kanj, and Warren B. Powell. "Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds." Operations Research 68, no. 6 (November 2020): 1678–97. http://dx.doi.org/10.1287/opre.2019.1939.

Повний текст джерела
Анотація:
In the paper, “Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds,” the authors propose an extension to Monte Carlo tree search that uses the idea of “sampling the future” to produce noisy upper bounds on nodes in the decision tree. These upper bounds can help guide the tree expansion process and produce decision trees that are deeper rather than wider, in effect concentrating computation toward more useful parts of the state space. The algorithm’s effectiveness is illustrated in a ride-sharing setting, where a driver/vehicle needs to make dynamic decisions regarding trip acceptance and relocations.
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5

Li, Jiawei, Yiming Li, Xingchun Xiang, Shu-Tao Xia, Siyi Dong, and Yun Cai. "TNT: An Interpretable Tree-Network-Tree Learning Framework using Knowledge Distillation." Entropy 22, no. 11 (October 24, 2020): 1203. http://dx.doi.org/10.3390/e22111203.

Повний текст джерела
Анотація:
Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenarios, such as medical or financial data mining and decision-making. In this paper, we propose a Tree-Network-Tree (TNT) learning framework for explainable decision-making, where the knowledge is alternately transferred between the tree model and DNNs. Specifically, the proposed TNT learning framework exerts the advantages of different models at different stages: (1) a novel James–Stein Decision Tree (JSDT) is proposed to generate better knowledge representations for DNNs, especially when the input data are in low-frequency or low-quality; (2) the DNNs output high-performing prediction result from the knowledge embedding inputs and behave as a teacher model for the following tree model; and (3) a novel distillable Gradient Boosted Decision Tree (dGBDT) is proposed to learn interpretable trees from the soft labels and make a comparable prediction as DNNs do. Extensive experiments on various machine learning tasks demonstrated the effectiveness of the proposed method.
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6

Heshmatol Vaezin, S. M., J. L. Peyron, and F. Lecocq. "A simple generalization of the Faustmann formula to tree level." Canadian Journal of Forest Research 39, no. 4 (April 2009): 699–711. http://dx.doi.org/10.1139/x08-202.

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Анотація:
The economic decision model serving as an objective function in forest economics was conceived originally by Faustmann at the stand level. Nevertheless, the tree level seems to be an appropriate scale for analysis, especially for harvesting decisions and certain estimations both at tree and stand levels. However, the Faustmann formula cannot be directly applied to the tree level. The present research has provided certain tree-level formulations of the Faustmann formula, including, in particular, tree expectation value (TEV) and land expectation value (LEV). TEV and tree-level LEV formulas were developed by analyzing the Faustmann formula under deterministic conditions. Unlike previous tree-level decision models presented in the forest economics literature, TEV and tree-level LEV formulas incorporate the expectation value of the land occupied by trees and its variability over time as well as the interaction between trees and their trajectories (cutting age). The proposed formulas were then compared with the Faustmann formula using the first-order condition of optimal harvest age. The TEV and tree-level LEV formulas appeared to be absolutely compatible with the Faustmann formula. The utility of the proposed formulas was then illustrated with application examples, including target diameter, stand expectation value, TEV, LEV, and the value of damage to beech trees or stands in northeastern France.
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7

Rautenberg, Tamlyn, Annette Gerritsen, and Martin Downes. "Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer." Diagnostics 10, no. 3 (March 14, 2020): 158. http://dx.doi.org/10.3390/diagnostics10030158.

Повний текст джерела
Анотація:
Health economics is a discipline of economics applied to health care. One method used in health economics is decision tree modelling, which extrapolates the cost and effectiveness of competing interventions over time. Such decision tree models are the basis of reimbursement decisions in countries using health technology assessment for decision making. In many instances, these competing interventions are diagnostic technologies. Despite a wealth of excellent resources describing the decision analysis of diagnostics, two critical errors persist: not including diagnostic test accuracy in the structure of decision trees and treating sequential diagnostics as independent. These errors have consequences for the accuracy of model results, and thereby impact on decision making. This paper sets out to overcome these errors using color to link fundamental epidemiological calculations to decision tree models in a visually and intuitively appealing pictorial format. The paper is a must-read for modelers developing decision trees in the area of diagnostics for the first time and decision makers reviewing diagnostic reimbursement models.
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8

Marzouk, Mohamed, and Emad Mohamed. "Modeling bid/no bid decisions using fuzzy fault tree." Construction Innovation 18, no. 1 (January 2, 2018): 90–108. http://dx.doi.org/10.1108/ci-11-2016-0060.

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Анотація:
Purpose Decisions by construction contractors to bid (or not to bid) require the thorough assessment and evaluation of factors relevant to the decision, as well as the quantification of their combined impact, to produce successful bid/no-bid decisions. The purpose of this study is to present a fuzzy fault tree model to assist construction contractors to more efficiently bid for future projects. Design/methodology/Approach The proposed model consist of two stages: first, identification of the factors that affect bidding decision using a questionnaire survey after an extensive literature review, and second, usage of the identified factors to build a fuzzy fault tree model to simulate the bidding decision. Findings A list of 15 factors that affect bid/no-bid decisions was identified. Analysis of factors revealed that the highest-ranking factors were related to financial aspects of the project. A case study is presented to demonstrate the capabilities of the model, and a fuzzy important analysis is performed on the basic events to demonstrate the differences between three contractors’ bid/no-bid decisions. The results reveal that there is variation between the decisions of each contractor based on their willingness to participate. Besides, the influence of evaluation factors on the final decision for each contractor is different. Originality/value The study contributes to the body of knowledge on tendering and bidding practices. The proposed model incorporated the fuzzy set theory, which suits human subjectivity. The proposed methodology overcomes the limitations of previous models as it can, using the linear pool opinion principle, combine and weigh the evaluations of multiple experts. In addition, the model is convenient for situations where historical data are not available.
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9

Abate, Tensaye, and Temesgen Yohannes. "Socio-Economic Determinants of Smallholder Tree Plantation in Basona-Werana Woreda in the North Shoa of Amhara Regional State, Ethiopia." Caraka Tani: Journal of Sustainable Agriculture 37, no. 1 (November 22, 2021): 15. http://dx.doi.org/10.20961/carakatani.v37i1.54247.

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Анотація:
Tree growing by smallholders is an emerging livelihood strategy in Basona-Werana <em>Woreda</em> of the North Shoa Zone of Amhara Regional State. The objective of this study was to identify socio-economic determinants of the smallholder tree growing in the study area. Data were collected from the household survey, key informants and focus group discussions. The binary logistic regression model was employed to identify the socio-economic determinants of smallholder tree growing behavior. According to the study, about 55% of tree growers generated their livelihood income from tree planting whereas 72% of non-growers generated income from livestock. Family size of the household and age positively and significantly affected tree planting decisions at P &lt; 0.10 and P &lt; 0.01, respectively. Meanwhile, livestock ownership and distance to the market were negatively and significantly influenced the decision to tree planting at P &lt; 0.01 and P &lt; 0.05, correspondingly. Similarly, total household income positively and significantly (P &lt; 0.01) affected tree planting decisions. This study concluded that the socio-economic circumstances of smallholder farmers must be taken into account in the formulation of initiatives and policies aimed at encouraging smallholders to grow trees in their farming systems to improve livelihood and sustainable agricultural production.
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10

Luna, José Marcio, Efstathios D. Gennatas, Lyle H. Ungar, Eric Eaton, Eric S. Diffenderfer, Shane T. Jensen, Charles B. Simone, Jerome H. Friedman, Timothy D. Solberg, and Gilmer Valdes. "Building more accurate decision trees with the additive tree." Proceedings of the National Academy of Sciences 116, no. 40 (September 16, 2019): 19887–93. http://dx.doi.org/10.1073/pnas.1816748116.

Повний текст джерела
Анотація:
The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.
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Більше джерел

Дисертації з теми "Tree of decisions"

1

Nilsson, Lannerstedt Katarina. "Location decisions regarding forest plantations in Brazil : Which aspects are important to actors in the Brazilian tree industry?" Thesis, KTH, Hållbar utveckling, miljövetenskap och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266973.

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Анотація:
Brazilian productive forest plantations and sustainability is a continuous subject of debate, since such forest plantations constitute an increasing proportion of the Brazilian forest cover, and because of documented instances where some establishments have resulted in negative impacts on local ecosystems and communities. Meanwhile, balancing economic, environmental and social sustainability is becoming an important concern in industrial decision-making given the increasing importance of the global sustainability goals. The aim of this study is to investigate the contemporary decision context in which companies in the Brazilian tree industry select locations for forest plantations in Brazil. Special focus is devoted to how sustainability aspects are included in such decisions, as well as the roles assigned to possible mechanisms for sustainable development, such as Brazilian policy, legislation and independent forest certification. A qualitative research strategy is employed, which encompasses a literature review and semi-structured interviews with industry practitioners in two segments of the industry, whose perceptions are triangulated with perspectives of relevant non-industry actors. Grounded theory is used to analyse the qualitative data. The findings from the literature and the qualitative data are synthesised, and several hypotheses are consequently developed regarding forest plantation location decisions and sustainability in Brazil. The hypotheses evolve around the finding that strategic, economic, environmental and social perspectives are perceived as present in contemporary decision-making, in certified pulp and paper companies. Moreover, a hypothesis of general character is developed based on the synthesised findings, which is that forest plantations can be the subject of integrated location and sustainability analyses, where the problem can be formulated as finding an optimal location for a forest plantation from a three-dimensional sustainability perspective. Finally, some prospects for further integrated research about forest plantation location and sustainability are presented.
Denna studie handlar om brasilianska skogsplanteringar och expansionen av planteringar som ägs av företag i landets, så kallade ”trädindustri”. Studien fokuserar på produktiva skogsplanteringar av introducerade arter, för vilka intresset har vuxit parallellt med att de har expanderat till yta under de senaste decennierna. Intresset har även vuxit eftersom federala beslutsfattare har lyft fram skogsplanteringar som ett verktyg för att minska landets koldioxidutsläpp och stimulera ekonomisk utveckling. Produktiva brasilianska skogsplanteringar och hållbarhet är ett kontinuerligt föremål för debatt. Den befintliga litteraturen om expansionen av sådana planteringar indikerar att företag i landets trädindustri inte alltid har balanserat de ekonomiska, miljömässiga och sociala hållbarhetsdimensionerna i sina beslut gällande var planteringar ska anläggas. I vissa fall har besluten resulterat i negativa följder för lokala ekosystem och samhällen. Vidare finns det, såvitt författaren vet, inga studier som behandlar lokaliseringsbeslut gällande sådana brasilianska skogsplanteringar och alla de tre hållbarhetsdimensionerna. Därför syftar denna studie till att förbättra förståelsen för det samtida beslutssammanhang i vilket företag i den brasilianska trädindustrin väljer platser för sina skogsplanteringar, samt hur hållbarhetsaspekter omfattas i sådana beslut. Som ett led i denna strävan undersöks möjliga platsfaktorer, rumsliga begränsningar och andra beslutsaspekter, liksom vilken roll företag tilldelar specifika mekanismer för hållbar utveckling, såsom brasiliansk policy, lagstiftning och oberoende skogscertifiering. En kvalitativ forskningsstrategi antas för att genomföra undersökningen. En litteraturöversikt kombineras med semistrukturerade intervjuer med branschutövare i två segment av den brasilianska trädindustrin. Deras uppfattningar trianguleras med perspektiv från relevanta aktörer utanför branschen. Urvalet av deltagarna för studien genomfördes på plats i Brasilien 2015 till 2016 och resulterade i 13 brasilianska intervjudeltagare. Intervjuerna genomfördes sedan på olika platser i Brasilien under 2016. Grundad teori används som forskningsmetod för att analysera insamlad kvalitativ data. Två huvudteman, flera sekundära teman och ett koncept härleds från de kvalitativa data som samlades in, vilka främst grundar sig i uppfattningar gällande brasilianska massa- och pappersföretag. Flera beslutsfaktorer och begränsningar som kan påverka placeringen av brasilianska skogsplanteringar identifieras också. Det kan konstateras att aktörerna i urvalet i studien uppfattar det som att strategiska, ekonomiska, miljömässiga och sociala perspektiv är närvarande i dagens lokaliseringsbeslut, utförda av certifierade, brasilianska massa- och pappersföretag. En viktig begränsning i studien är att dess utforskande karaktär hindrar författaren från att presentera några ”sanningar” om ämnet som undersöks, eller att dra slutsatser om företagens verkliga handlingar. Därför presenteras istället ett antal hypoteser som gäller Brasilien, men även en hypotes av generell karaktär. Den generella hypotesen är att skogsplanteringar kan vara föremål för integrerad lokaliserings- och hållbarhetsanalys, där problemet kan formuleras som att hitta optimala platser för skogsplanteringar ur ett tredimensionellt hållbarhetsperspektiv. Med tanke på begränsningarna i studien, samt associerade osäkerheter som hypoteserna gällande det brasilianska fallet är befästa med, är rekommendationen att fortsatta studier först koncentrerar sig på att testa den allmänna hypotesen. Om fortsatta studier på nationell nivå visar sig vara fördelaktiga efter sådana tester, uppmuntras forskare att återvända till sammanfattningen av branschperspektiv, återstående frågor och hypoteser som tillgängliggörs för fortsatt forskning om Brasilien genom denna studie.
Este estudo trata das florestas plantadas brasileiras, e da expansão de plantios pertencentes a empresas da indústria brasileira de árvores plantadas. O estudo tem foco nas florestas plantadas produtivas de espécies introduzidas, pelas quais se teve um aumento no interesse paralelamente à sua expansão geográfica nas últimas décadas. O interesse também aumentou ao destaque dado aos plantios florestais pelo governo federal como uma ferramenta para reduzir as emissões de dióxido de carbono do país, e estimular o desenvolvimento econômico. As florestas plantadas produtivas brasileiras e a sustentabilidade são constantemente temas de debate. A literatura existente sobre essa expansão indica que as empresas da indústria brasileira de árvores plantadas nem sempre equilibram as três dimensões de sustentabilidade ao decidir onde plantar suas florestas. Em alguns casos as decisões resultam em impactos negativos nos ecossistemas e comunidades locais. Além disso, com base no conhecimento da autora, não existem estudos que tratem das decisões de localização das florestas plantadas brasileiras e de todas essas três dimensões da sustentabilidade. Por tanto, este estudo tem como objetivo melhorar a compreensão do contexto atual de tomada de decisões em que as empresas na indústria brasileira de árvores plantadas escolhem os locais para suas florestas plantadas, e como os aspectos de sustentabilidade são incluídos em tais decisões. Como parte desse empenho, são examinadas possíveis limitações e outros aspectos de tomada de decisão, bem como o papel que as empresas atribuem à certos mecanismos para o desenvolvimento sustentável, como a política brasileira, a legislação e a certificação florestal. Uma estratégia de pesquisa qualitativa é adotada para conduzir a pesquisa. Uma revisão de literatura é combinada com entrevistas semiestruturadas com profissionais em dois segmentos da indústria brasileira de árvores plantadas. Suas percepções são trianguladas com perspectivas de atores relevantes de fora da indústria. A amostra de participantes do estudo foi realizada no Brasil entre 2015 e 2016 e resultou em 13 participantes. As entrevistas foram então realizadas em diversos locais no Brasil em 2016. É utilizada a teoria fundamentada nos dados como método de pesquisa para analisar os dados qualitativos coletados. São extraídos dois temas principais, diversos temas secundários e um conceito a partir dos dados qualitativos coletados, baseados principalmente nas percepções das empresas brasileiras de papel e celulose. Vários fatores de decisão que podem influenciar a localização das plantações florestais também são identificados. Observa-se que os participantes da amostra do estudo percebem que perspectivas estratégicas, econômicas, ambientais e sociais estão presentes nas decisões atuais de localização, realizadas por empresas certificadas de celulose e papel. Uma importante limitação do estudo é que sua característica exploratória impede que a pesquisadora apresente “verdades” sobre o assunto investigado, ou tire conclusões sobre os atos das empresas. Portanto, são apresentadas várias hipóteses aplicáveis ao Brasil, mas também uma hipótese de caráter geral. A hipótese geral é que as plantações florestais podem estar sujeitas a análises integradas de localização e sustentabilidade, onde o problema pode ser formulado como encontrar um local ideal para uma plantação florestal a partir de uma perspectiva tridimensional de sustentabilidade. Dadas as limitações do estudo, bem como as incertezas associadas às quais as hipóteses do Brasil estão relacionadas, a recomendação é que novos estudos se concentrem primeiro em testar a hipótese geral. Caso novos estudos em nível nacional forem benéficos após esses testes, os pesquisadores são incentivados a retornar ao resumo das perspectivas da indústria, das questões remanescentes e das hipóteses disponibilizadas para futuras pesquisas sobre o Brasil por meio deste estudo.
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2

Klinka, Karel, Pal Varga, and Christine Chourmouzis. "Select CD : computer support system for making tree species and reproduction cutting decisions in the coastal forest of BC." Forest Sciences Department, University of British Columbia, 1999. http://hdl.handle.net/2429/672.

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Анотація:
"SELECT CD is a site-specific, decision-support tool for selecting ecologically viable tree species, reproduction cuttings, and regeneration methods in the coastal forest (CDF, CWH, and MH zones). SELECT CD integrates information from several existing guides with new information from literature and recent research into a single, user-friendly resource. SELECT CD also includes a rich library of visuals and an illustrated glossary of technical terms."
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3

Bogdan, Vukobratović. "Hardware Acceleration of Nonincremental Algorithms for the Induction of Decision Trees and Decision Tree Ensembles." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=102520&source=NDLTD&language=en.

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Анотація:
The thesis proposes novel full decision tree and decision tree ensembleinduction algorithms EFTI and EEFTI, and various possibilities for theirimplementations are explored. The experiments show that the proposed EFTIalgorithm is able to infer much smaller DTs on average, without thesignificant loss in accuracy, when compared to the top-down incremental DTinducers. On the other hand, when compared to other full tree inductionalgorithms, it was able to produce more accurate DTs, with similar sizes, inshorter times. Also, the hardware architectures for acceleration of thesealgorithms (EFTIP and EEFTIP) are proposed and it is shown in experimentsthat they can offer substantial speedups.
У овоj дисертациjи, представљени су нови алгоритми EFTI и EEFTI заформирање стабала одлуке и њихових ансамбала неинкременталномметодом, као и разне могућности за њихову имплементациjу.Експерименти показуjу да jе предложени EFTI алгоритам у могућностида произведе драстично мања стабла без губитка тачности у односу напостојеће top-down инкременталне алгоритме, а стабла знатно већетачности у односу на постојеће неинкременталне алгоритме. Такође супредложене хардверске архитектуре за акцелерацију ових алгоритама(EFTIP и EEFTIP) и показано је да је уз помоћ ових архитектура могућеостварити знатна убрзања.
U ovoj disertaciji, predstavljeni su novi algoritmi EFTI i EEFTI zaformiranje stabala odluke i njihovih ansambala neinkrementalnommetodom, kao i razne mogućnosti za njihovu implementaciju.Eksperimenti pokazuju da je predloženi EFTI algoritam u mogućnostida proizvede drastično manja stabla bez gubitka tačnosti u odnosu napostojeće top-down inkrementalne algoritme, a stabla znatno većetačnosti u odnosu na postojeće neinkrementalne algoritme. Takođe supredložene hardverske arhitekture za akceleraciju ovih algoritama(EFTIP i EEFTIP) i pokazano je da je uz pomoć ovih arhitektura mogućeostvariti znatna ubrzanja.
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4

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

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Анотація:
Decision trees (DTs) play a vital role in statistical modelling. Simplicity and interpretability of the solution structure have made the method popular in a wide range of disciplines. In data classification problems, DTs recursively partition the feature space into disjoint sub-regions until each sub-region becomes homogeneous with respect to a particular class. Axis parallel splits, the simplest form of splits, partition the feature space parallel to feature axes. However, for some problem domains DTs with axis parallel splits can produce complicated boundary structures. As an alternative, oblique splits are used to partition the feature space potentially simplifying the boundary structure. Various approaches have been explored to find optimal oblique splits. One approach is based on optimisation techniques. This is considered the benchmark approach, however, its major limitation is that the tree induction algorithm is computationally expensive. On the other hand, split finding approaches based on heuristic arguments have gained popularity and have made improvements on benchmark methods. This thesis proposes a methodology to induce oblique decision trees in transformed spaces based on a heuristic argument. As the first goal of the thesis, a new oblique decision tree algorithm, called HHCART (\underline{H}ouse\underline{H}older \underline{C}lassification and \underline{R}egression \underline{T}ree) is proposed. The proposed algorithm utilises a series of Householder matrices to reflect the training data at each non-terminal node during the tree construction. Householder matrices are constructed using the eigenvectors from each classes' covariance matrix. Axis parallel splits in the reflected (or transformed) spaces provide an efficient way of finding oblique splits in the original space. Experimental results show that the accuracy and size of the HHCART trees are comparable with some benchmark methods in the literature. The appealing features of HHCART is that it can handle both qualitative and quantitative features in the same oblique split, conceptually simple and computationally efficient. Data mining applications often come with massive example sets and inducing oblique DTs for such example sets often consumes considerable time. HHCART is a serial computing memory resident algorithm which may be ineffective when handling massive example sets. As the second goal of the thesis parallel computing and disk resident versions of the HHCART algorithm are presented so that HHCART can be used irrespective of the size of the problem. HHCART is a flexible algorithm and the eigenvectors defining Householder matrices can be replaced by other vectors deemed effective in oblique split finding. The third endeavour of this thesis explores this aspect of HHCART. HHCART can be used with other vectors in order to improve classification results. For example, a normal vector of the angular bisector, introduced in the Geometric Decision Tree (GDT) algorithm, is used to construct the Householder reflection matrix. The proposed method produces better results than GDT for some problem domains. In the second case, \textit{Class Representative Vectors} are introduced and used to construct Householder reflection matrices. The results of this experiment show that these oblique trees produce classification results competitive with those achieved with some benchmark decision trees. DTs are constructed using two approaches, namely: top-down and bottom-up. HHCART is a top-down tree, which is the most common approach. As the fourth idea of the thesis, the concept of HHCART is used to induce a new DT, HHBUT, using the bottom-up approach. The bottom-up approach performs cluster analysis prior to the tree building to identify the terminal nodes. The use of the Bayesian Information Criterion (BIC) to determine the number of clusters leads to accurate and compact trees when compared with Cross Validation (CV) based bottom-up trees. We suggest that HHBUT is a good alternative to the existing bottom-up tree especially when the number of examples is much higher than the number of features.
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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 performed this way to compare.
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6

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 selection criteria for decision tree building algorithm. The search is conducted by a genetic algorithm. The result of the hyper-heuristic search in this case is a strategy for selecting attributes while building decision trees. Most hyper-heuristics work by trying to adapt the heuristic to the state of the problem being solved. Our hyper-heuristic is no different. It employs a strategy for adapting the heuristic used to build decision tree nodes according to some set of features of the training set it is working on. We introduce, explore and evaluate five different ways in which this problem state can be represented for a hyper-heuristic that operates within a decisiontree building algorithm. In each case, the hyper-heuristic is guided by a rule set that tries to map features of the data set to be split by the decision tree building algorithm to a heuristic to be used for splitting the same data set. We also explore and evaluate three different sets of low-level heuristics that could be employed by such a hyper-heuristic. This work also makes a distinction between specialist hyper-heuristics and generalist hyper-heuristics. The main difference between these two hyperheuristcs is the number of training sets used by the hyper-heuristic genetic algorithm. Specialist hyper-heuristics are created using a single data set from a particular domain for evolving the hyper-heurisic rule set. Such algorithms are expected to outperform standard algorithms on the kind of data set used by the hyper-heuristic genetic algorithm. Generalist hyper-heuristics are trained on multiple data sets from different domains and are expected to deliver a robust and competitive performance over these data sets when compared to standard algorithms. We evaluate both approaches for each kind of hyper-heuristic presented in this thesis. We use both real data sets as well as synthetic data sets. Our results suggest that none of the hyper-heuristics presented in this work are suited for specialization – in most cases, the hyper-heuristic’s performance on the data set it was specialized for was not significantly better than that of the best performing standard algorithm. On the other hand, the generalist hyper-heuristics delivered results that were very competitive to the best standard methods. In some cases we even achieved a significantly better overall performance than all of the standard methods.
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Hari, Vijaya. "Empirical Investigation of CART and Decision Tree Extraction from Neural Networks." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1235676338.

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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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10

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
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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Книги з теми "Tree of decisions"

1

Reed, W. J. Planting decisions in the face of uncertainty. Vancouver, B.C: Forest Economics and Policy Analysis Research Unit, University of British Columbia, 1991.

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2

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

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3

Gladwin, Christina. Ethnographic decision tree modeling. Newbury Park: Sage, 1989.

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4

Gladwin, Christina H. Ethnographic decision tree modeling. Newbury Park: Sage, 1989.

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5

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

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6

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

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7

Kustra, Rafal. Soft decision trees. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1999.

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8

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

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Authority, Financial Services. Stakeholder pensions decision trees. London: Financial Services Authority, 2000.

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10

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

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Частини книг з теми "Tree of decisions"

1

Liu, Yangyang, Jiucheng Xu, Lin Sun, and Lina Du. "Decisions Tree Learning Method Based on Three-Way Decisions." In Lecture Notes in Computer Science, 389–400. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25783-9_35.

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2

Silva, Andreia, and Cláudia Antunes. "Pushing Constraints into a Pattern-Tree." In Modeling Decisions for Artificial Intelligence, 139–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41550-0_13.

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Rodrigues, José F., Agma J. M. Traina, and Caetano Traina. "Visualization Tree, Multiple Linked Analytical Decisions." In Smart Graphics, 65–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11536482_6.

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4

Xu, Tao, Yun Zhou, Alexander Raake, and Xuyun Zhang. "Analyzing Impact Factors for Smartphone Sharing Decisions Using Decision Tree." In Lecture Notes in Computer Science, 628–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91244-8_48.

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5

Hofmockel, Kirsten, Curtis J. Richardson, and Patrick N. Halpin. "Effects of Hydrologic Management Decisions on Everglades Tree Islands." In Everglades Experiments, 191–214. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-68923-4_8.

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6

van Ommen, Thijs, Wouter M. Koolen, and Peter D. Grünwald. "Efficient Algorithms for Minimax Decisions Under Tree-Structured Incompleteness." In Lecture Notes in Computer Science, 336–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29765-7_28.

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7

Gustafsson, Janne, Ahti Salo, and Tommi Gustafsson. "PRIME Decisions: An Interactive Tool for Value Tree Analysis." In Lecture Notes in Economics and Mathematical Systems, 165–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56680-6_15.

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8

Katagiri, Hideki, Tomohiro Hayashida, Ichiro Nishizaki, and Jun Ishimatsu. "A Hybrid Algorithm Based on Tabu Search and Ant Colony Optimization for k-Minimum Spanning Tree Problems." In Modeling Decisions for Artificial Intelligence, 315–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04820-3_29.

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9

Lee, Dong-Liang, Lawrence Y. Deng, Kung-Huang Lin, You-Syun Jheng, Yung-Hui Chen, Chih-Yang Chao, and Jiung-Yao Huang. "Using Decision Tree Analysis for Personality to Decisions of the National Skills Competition Participants." In Lecture Notes in Electrical Engineering, 683–91. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6996-0_72.

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10

Carroll, J. Douglas, and Geert De Soete. "Fitting a Quasi-Poisson Case of the GSTUN (General Stochastic Tree UNfolding) Model and Some Extensions." In Knowledge, Data and Computer-Assisted Decisions, 93–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84218-4_7.

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Тези доповідей конференцій з теми "Tree of decisions"

1

Huang, Sieh-Chuen, Hsuan-Lei Shao, and Robert B. Leflar. "Applying decision tree analysis to family court decisions." In ICAIL '21: Eighteenth International Conference for Artificial Intelligence and Law. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3462757.3466076.

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Lerner, Scott, and Baris Taskin. "Towards Design Decisions for Genetic Algorithms in Clock Tree Synthesis." In 2018 Ninth International Green and Sustainable Computing Conference (IGSC). IEEE, 2018. http://dx.doi.org/10.1109/igcc.2018.8752170.

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3

Rego, Paulo A. L., Elaine Cheong, Emanuel F. Coutinho, Fernando A. M. Trinta, Masum Z. Hasan, and Jose N. de Souza. "Decision Tree-Based Approaches for Handling Offloading Decisions and Performing Adaptive Monitoring in MCC Systems." In 2017 5th IEEE International Conference on Mobile Cloud Computing, Services and Engineering (MobileCloud). IEEE, 2017. http://dx.doi.org/10.1109/mobilecloud.2017.19.

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4

Coles, Garill A., and Michael D. Zentner. "Application of Event Tree/Fault Tree Modeling Approach to the Evaluation of Proliferation Resistance." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43100.

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With the increasing popularity of nuclear energy as a means to address dwindling fossil resources and generation of greenhouse gases, comes a concern over the potential for increased proliferation of nuclear materials. Newly proposed processes/facilities will require an efficient, timely, and systematic assessment of the potential for proliferation to support decisions about optimum solutions to these competing concerns. Currently, evaluation of proliferation resistance is largely done by experts in an informal and unstructured fashion. As a result the evaluation may not always be repeatable or supportable. In 2002 an expert group was formed by The Generation IV International Forum to develop an internationally accepted methodology for assessing the proliferation resistance of a nuclear energy system (NES) and its individual elements. A pilot study was performed to test the methodologies being developed. The pilot consisted of assessing the proliferation resistance of a specific NES example with each analysis method. The example chosen was called the Example Sodium Fast Reactor which was designed to accept spent sodium-bonded, metallic fuel from four advanced fast reactors and to convert it into new fuel assemblies. A number of different approaches were taken to evaluate the diversion of material from the facility to assess their applicability and usefulness. This paper describes a Decision/Event Tree and Fault Tree Analysis approach.
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Su, BaoHe. "A Tree-based Concept Drift Detection Method by Three-way Decisions." In 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/amcce-17.2017.28.

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6

Brati Favarin, Samuel, and Rafael Ballottin Martins. "Aplicação de Mineração de Dados para o Auxílio da Tomada de Decisão em Gestão de Pessoas." In Computer on the Beach. Itajaí: Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p028-030.

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People are the foundation of organizations. For companies remain competitive, they need to develop and maintain their human resources. Professionals of the area, must rely on data to make their decisions, otherwise, it can generate bad decisions, taken only by intuition or experience. In this context, this project aimed to help the future decision making process made by human resource specialists of a People Management Software Company using the KDD process to generate new knowledge. In the data mining stage were used The Decision Tree, Neural Network, APRIORI and K-Means algorithms, generating patterns to be analysed with human resource specialists. Preliminary results demonstrate that it is possible to observe standards that classify employees as highly engaged, engaged, neutral and disengaged.
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7

Ranaweera, Nesha, Amila Jayasinghe, and Chethika Abenayake. "Decision tree application for model built-up land fragmentation in urban areas." In ERU Symposium 2021. Engineering Research Unit (ERU), University of Moratuwa, 2021. http://dx.doi.org/10.31705/eru.2021.1.

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Land fragmentation can define as the “situation where one area/unit is composed of a large number of parcels that are too small for their rational utilization” [5]. Land fragmentation affects sustainable development through its multiple impacts on environmental, economic, and social costs [13]. Effective land use management and policy decisions are always based on understanding, modeling, and predicting land-use changes in cities [9]. Therefore, the land fragmentation process should systematically investigate to provide a wide-ranging set of land use indicators to support sustainable development [12]. Built-up land fragmentation is the fragmentation or division of the built-up plots or units within the built-up land-use area horizontally. The objective of this study is to frame a Decision Tree (DT) model to identify the non-linear relationships between the Level of Built-up Land Fragmentation (LBLF) and its influencing factors in urban areas. The sub-objective is to quantify the LBLF in the Western Province, Sri Lanka. The study scope limits to LBLF and Decision Tree (DT) non-linear classifier. The study further quantifies the LBLF from 2000 to 2010 in Western Province, Sri Lanka as an initiation to frame the DT model.
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Zhou, Ruihua. "Research on Investment Decisions of Open-ended Funds Based on Decision Tree, RF and LGBM during COVID-19." In 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022). Paris, France: Atlantis Press, 2022. http://dx.doi.org/10.2991/aebmr.k.220405.024.

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9

Sullivan, G. J., and R. L. Baker. "Rate-distortion optimization for tree-structured source coding with multi-way node decisions." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226193.

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10

Van Bossuyt, Douglas, Chris Hoyle, Irem Y. Tumer, Andy Dong, Toni Doolen, and Richard Malak. "Toward Considering Risk Attitudes in Engineering Organizations Using Utility Theory." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70399.

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Design projects within large engineering organizations involve numerous uncertainties that can lead to unacceptably high levels of risk. Practicing designers recognize the existence of risk and commonly are aware of events that raise risk levels. However, a disconnect exists between past project performance and current project execution that limits decision-making. This disconnect is primarily due to a lack of quantitative models that can be used for rational decision-making. Methods and tools used to make decisions in risk-informed design generally use an expected value approach. Research in the psychology domain has shown that decision-makers and stakeholders have domain-specific risk attitudes that often have variations between individuals and between companies. Risk methods used in engineering such as Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and others are often ill-equipped to help stakeholders make decisions based upon risk-tolerant or risk-averse decision-making conditions. This paper focuses on the specific issue of helping stakeholders make decisions under risk-tolerant or risk-averse decision-making conditions and presents a novel method of translating engineering risk data from the domain of expected value into a domain corrected for risk attitude. This is done by using risk utility functions derived from the Engineering-Domain-Specific Risk-Taking (E-DOSPERT) test. This method allows decisions to be made based upon data that is risk attitude corrected. Further, the method uses an aspirational measure of risk attitude as opposed to existing lottery methods of generating utility functions that are based upon past performance. An illustrative test case using a simplified space mission designed in a collaborative design center environment is included. The method is shown to change risk-informed decisions in certain situations where a risk-tolerant or risk-averse decision-maker would likely choose differently than the dictates of the expected value approach.
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Звіти організацій з теми "Tree of decisions"

1

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

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3

Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Narlikar, Girija J. A Parallel, Multithreaded Decision Tree Builder. Fort Belvoir, VA: Defense Technical Information Center, December 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), September 2003. http://dx.doi.org/10.2172/815649.

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Mughal, Mohamed. Biological Weapons Response Template and Decision Tree. Fort Belvoir, VA: Defense Technical Information Center, April 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. Fort Belvoir, VA: Defense Technical Information Center, September 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. Ames: Iowa State University, Digital Repository, November 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), April 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. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada489077.

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