scholarly journals Decision Trees for Predicting the Physiological Responses of Rabbits

Animals ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 994
Author(s):  
Patrícia Ferreira Ponciano Ferraz ◽  
Yamid Fabián Hernández-Julio ◽  
Gabriel Araújo e Silva Ferraz ◽  
Raquel Silva de Moura ◽  
Giuseppe Rossi ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Trees ◽  
Environmental Variables ◽  
Thermal Environment ◽  
Physiological Responses ◽  
Rapid Evaluation ◽  
Production Environment ◽  
Experimental Database ◽  
Tree Models ◽  
Data Points

The thermal environment inside a rabbit house affects the physiological responses and consequently the production of the animals. Thus, models are needed to assist rabbit producers in decision-making to maintain the production environment within the zone of thermoneutrality for the animals. The aim of this paper is to develop decision trees to predict the physiological responses of rabbits based on environmental variables. The experiment was performed in a rabbit house with 26 rabbits at eight weeks of age. The experimental database is composed of 546 observed data points. Sixty decision tree models for the prediction of respiratory rate (RR, mov.min−1) and ear temperature (ET, °C) of rabbits exposed to different combinations of dry bulb temperature (tdb, °C) and relative humidity (RH, %) were developed. The ET model exhibited better statistical indices than the RR model. The developed decision trees can be used in practical situations to provide a rapid evaluation of rabbit welfare conditions based on environmental variables and physiological responses. This information can be obtained in real time and may help rabbit breeders in decision-making to provide satisfactory environmental conditions for rabbits.

scholarly journals Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 158
Author(s):  
Tamlyn Rautenberg ◽  
Annette Gerritsen ◽  
Martin Downes
Keyword(s):  
Decision Making ◽  
Health Economics ◽  
Decision Tree ◽  
Decision Trees ◽  
Decision Makers ◽  
Test Accuracy ◽  
Tree Models ◽  
The Cost ◽  
Diagnostic Technologies ◽  
First Time

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.

A Practical Tutorial for Decision Tree Induction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-38
Author(s):  
Víctor Adrián Sosa Hernández ◽  
Raúl Monroy ◽  
Miguel Angel Medina-Pérez ◽  
Octavio Loyola-González ◽  
Francisco Herrera
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Machine Learning Techniques ◽  
Evaluation Measures ◽  
Decision Tree Induction ◽  
Learning Techniques ◽  
Tree Models ◽  
Evaluation Measure ◽  
Main Components ◽  
Support Decision Making

Experts from different domains have resorted to machine learning techniques to produce explainable models that support decision-making. Among existing techniques, decision trees have been useful in many application domains for classification. Decision trees can make decisions in a language that is closer to that of the experts. Many researchers have attempted to create better decision tree models by improving the components of the induction algorithm. One of the main components that have been studied and improved is the evaluation measure for candidate splits. In this article, we introduce a tutorial that explains decision tree induction. Then, we present an experimental framework to assess the performance of 21 evaluation measures that produce different C4.5 variants considering 110 databases, two performance measures, and 10× 10-fold cross-validation. Furthermore, we compare and rank the evaluation measures by using a Bayesian statistical analysis. From our experimental results, we present the first two performance rankings in the literature of C4.5 variants. Moreover, we organize the evaluation measures into two groups according to their performance. Finally, we introduce meta-models that automatically determine the group of evaluation measures to produce a C4.5 variant for a new database and some further opportunities for decision tree models.

scholarly journals Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2849
Author(s):  
Sungbum Jun
Keyword(s):  
Decision Tree ◽  
Evolutionary Algorithm ◽  
Decision Trees ◽  
Manufacturing Systems ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Industrial Internet ◽  
Tree Models ◽  
Real World Datasets

Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.

The Use of Tree Models for Silvicultural Decision Making

2006 ◽  
pp. 237-261 ◽  
Author(s):  
Matthias Schmidt ◽  
Thomas Böckmann ◽  
Jürgen Nagel
Keyword(s):  
Decision Making ◽  
Tree Models

Data Science Tools Application for Business Processes Modelling in Aviation

2019 ◽  
pp. 176-190
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

Models of Decision-Making Operators of Socio-Technical System

2017 ◽  
pp. 33-75 ◽  
Author(s):  
Tetiana Shmelova ◽  
Yuliya Sikirda
Keyword(s):  
Decision Making ◽  
Stochastic Models ◽  
Network Models ◽  
Technical System ◽  
Behavioral Activity ◽  
Neural Network Models ◽  
Markov Network ◽  
The Neural Network ◽  
Tree Models ◽  
Aerial Vehicle

In this chapter, the authors present Air Navigation System (ANS) as a Socio-technical System (STS). The authors present models of decision making (DM) operators of STS, such as the deterministic models obtained for using network planning; the stochastic models obtained for using decision-tree; models in uncertainty obtained for using criteria Vald, Laplace, Savage, Hurwicz and other. The authors presented also DM models of operators in ANS, such as the neural network models, fuzzy models, the Markov network models, GERT-models for modelling and forecasting of behavioral activity of ANS's Human-operator (H-O) in flight emergencies situation. The scenarios of developing a flight situation in case of selecting either the positive or negative pole in accordance with the reflexive theory have been obtained. They demonstrate some examples with DM's deterministic and stochastic models for engineers, pilots, air traffic controllers, Unmanned Aerial Vehicle (UAV) operators, managers etc. In addition, the chapter presents some examples of DM models developed by the author and students at National Aviation University.

Utilizing Fuzzy Decision Trees in Decision Making

2011 ◽  
pp. 2024-2030
Author(s):  
Malcolm J. Beynonm
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Decision Trees ◽  
Time Series Data ◽  
Series Data ◽  
Fuzzy Decision ◽  
Fuzzy Environment ◽  
Complex Decision Making ◽  
Fuzzy Decision Trees

The seminal work of Zadeh (1965), namely fuzzy set theory (FST), has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability to the antecedents and consequents identified for object classification (See Breiman, 2001). Within a fuzzy environment this is extended to allow a linguistic facet to the possible interpretation, examples including mining time series data (Chiang, Chow, & Wang, 2000) and multi-objective optimisation (Ishibuchi & Yamamoto, 2004). One approach to if-then rule construction has been through the use of decision trees (Quinlan, 1986), where the path down a branch of a decision tree (through a series of nodes), is associated with a single if-then rule. A key characteristic of the traditional decision tree analysis is that the antecedents described in the nodes are crisp, where this restriction is mitigated when operating in a fuzzy environment (Crockett, Bandar, Mclean, & O’Shea, 2006). This chapter investigates the use of fuzzy decision trees as an effective tool for data mining. Pertinent to data mining and decision making, Mitra, Konwar and Pal (2002) succinctly describe a most important feature of decision trees, crisp and fuzzy, which is their capability to break down a complex decision-making process into a collection of simpler decisions and thereby, providing an easily interpretable solution.

Data Science Tools Application for Business Processes Modelling in Aviation

2021 ◽  
pp. 617-631
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

scholarly journals Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making

2019 ◽  
Vol 33 ◽  
pp. 1418-1426 ◽  
Author(s):  
Sina Aghaei ◽  
Mohammad Javad Azizi ◽  
Phebe Vayanos
Keyword(s):  
Decision Making ◽  
Decision Trees ◽  
Mixed Integer ◽  
Training Dataset ◽  
Integer Optimization ◽  
Disparate Impact ◽  
Optimization Framework ◽  
Disparate Treatment ◽  
Misclassification Rates ◽  
Automated Tools

In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the production of entertainment). More recently, these algorithms are increasingly being used to assist socially sensitive decisionmaking (e.g., to decide who to admit into a degree program or to prioritize individuals for public housing). Yet, these automated tools may result in discriminative decision-making in the sense that they may treat individuals unfairly or unequally based on membership to a category or a minority, resulting in disparate treatment or disparate impact and violating both moral and ethical standards. This may happen when the training dataset is itself biased (e.g., if individuals belonging to a particular group have historically been discriminated upon). However, it may also happen when the training dataset is unbiased, if the errors made by the system affect individuals belonging to a category or minority differently (e.g., if misclassification rates for Blacks are higher than for Whites). In this paper, we unify the definitions of unfairness across classification and regression. We propose a versatile mixed-integer optimization framework for learning optimal and fair decision trees and variants thereof to prevent disparate treatment and/or disparate impact as appropriate. This translates to a flexible schema for designing fair and interpretable policies suitable for socially sensitive decision-making. We conduct extensive computational studies that show that our framework improves the state-of-the-art in the field (which typically relies on heuristics) to yield non-discriminative decisions at lower cost to overall accuracy.

Strategic Decision Making in Intergenerational Successions of Small- and Medium-Size Family-Owned Businesses

2001 ◽  
Vol 14 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Per-Olof Bjuggren ◽  
Lars-Göran Sund
Keyword(s):  
Decision Making ◽  
Decision Trees ◽  
Family Business ◽  
Legal System ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Medium Size ◽  
Strategic Decisions ◽  
The Family ◽  
Transition Modes

This paper deals with intergenerational successions of small and medium-size enterprises (SMEs). Entrepreneurs face an unavoidable succession dilemma: they must make either explicit or implicit strategic decisions about transitioning ownership of the family business. The main alternatives are to sell the company to someone outside the family or to make arrangements for an interfamily succession. In the latter case, there are many transition modes, e.g., through a gift of shares or a will. This paper uses decision trees to analyze intergenerational successions problems. One conclusion of the paper is that it is important for a society to provide a legal system that facilitates transitions of family companies within the family because the legal system will, among other positive factors connected with family businesses, preserve idiosyncratic knowledge of family character.

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