Using Decision Trees to Identify Tourism Stakeholders: The Case of Two Eastern North Carolina Counties

2007 ◽  
Vol 7 (3-4) ◽  
pp. 176-193 ◽  
Author(s):  
Erick T. Byrd ◽  
Larry Gustke
Keyword(s):  
North Carolina ◽  
Rural Communities ◽  
Decision Tree ◽  
Planning Process ◽  
Stakeholder Involvement ◽  
Tourism Planning ◽  
Chi Square ◽  
Analytical Technique ◽  
Sustainable Tourism Development ◽  
Interaction Detection

This paper explores stakeholder involvement in tourism planning, development, and management. For tourism planners to include stakeholders in the tourism planning process those stakeholders and their interests need to be identified. The research reported in this paper describes and applies an analytical technique that is not traditionally used to identify stakeholders. A questionnaire was developed and mailed to stakeholders in two rural communities in North Carolina. The data were analysed with an Exhaustive Chi-square Automatic Interaction Detection decision tree. From the results of the decision tree, stakeholder groups were identified in relation to their support for sustainable tourism development in their community.

Stakeholders' involvement in sustainability planning and implementation: the case of Cyprus

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Iordanis Katemliadis ◽  
Georgios Markatos
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Design Methodology ◽  
Stakeholder Involvement ◽  
Tourism Planning ◽  
Content Type ◽  
Sustainable Tourism Development ◽  
Sustainability Planning ◽  
Active Involvement

PurposeTourism planning and development has revolved around sustainability concepts and issues, and this paper aims to provide an analysis of stakeholder involvement in sustainability planning and implementation in Cyprus.Design/methodology/approachThe article provides a comprehensive perspective on stakeholder involvement in sustainability planning and implementation based on a systematic literature review.FindingsStudy findings indicate that the active involvement of stakeholders is a prerequisite in order to address the complex issues of sustainable tourism development.Originality/valueThe authors examined the role of stakeholders at individual, local and international levels, and how they can make a difference in transitioning to a more sustainable future for tourism in Cyprus.

Predicting peri‐implant disease: Chi‐square automatic interaction detection (CHAID) decision tree analysis of risk indicators

2019 ◽  
Vol 90 (8) ◽  
pp. 834-846 ◽  
Author(s):  
Momen A. Atieh ◽  
Ju Keat Pang ◽  
Kylie Lian ◽  
Stephanie Wong ◽  
Andrew Tawse‐Smith ◽  
...  
Keyword(s):  
Decision Tree ◽  
Risk Indicators ◽  
Decision Tree Analysis ◽  
Chi Square ◽  
Tree Analysis ◽  
Interaction Detection

scholarly journals Detection of Subclinical Keratoconus Using a Novel Combined Tomographic and Biomechanical Model Based on an Automated Decision Tree

2021 ◽  
Author(s):  
Peng Song ◽  
Shengwei Ren ◽  
Yu Liu ◽  
Pei Li ◽  
Qingyan Zeng
Keyword(s):  
Decision Tree ◽  
Amplitude Ratio ◽  
Regression Tree ◽  
Biomechanical Model ◽  
Study Data ◽  
Classification And Regression Tree ◽  
Stiffness Parameter ◽  
Chi Square ◽  
Interaction Detection ◽  
Cart Model

Abstract The aim of this study was to develop a predictive model for subclinical keratoconus (SKC) based on decision tree (DT) algorithms. A total of 194 eyes (including 105 normal eyes and 89 SKC) were included in the double-center retrospective study. Data were separately used for training and validation databases. The baseline variables were derived from tomography and biomechanical imaging. DT models were generated in the training database using Chi-square automatic interaction detection (CHAID) and classification and regression tree (CART) algorithms. The discriminating rules of the CART model selected variables of the Belin/Ambrósio deviation (BAD-D), stiffness parameter at first applanation (SPA1), back eccentricity (Becc), and maximum pachymetric progression index in order, while the CHAID model selected BAD-D, deformation amplitude ratio, SPA1, and Becc. The CART model allowed discrimination between normal and SKC eyes with 92.2% accuracy, which was higher than that of the CHAID model (88.3%), BAD-D (82.0%), Corvis biomechanical index (CBI, 77.3%), and tomographic and biomechanical index (TBI, 78.1%). The discriminating performance of the CART model was validated with 92.4% accuracy, while the CHAID model was validated with 86.4% accuracy in the validation database. Thus, the CART model using tomography and biomechanical imaging was an excellent model for SKC screening and provided easy-to-understand discriminating rules.

scholarly journals Machine Learning Classifiers for Modeling Soil Characteristics by Geophysics Investigations: A Comparative Study

2020 ◽  
Vol 10 (17) ◽  
pp. 5734
Author(s):  
Chee Soon Lim ◽  
Edy Tonnizam Mohamad ◽  
Mohammad Reza Motahari ◽  
Danial Jahed Armaghani ◽  
Rosli Saad
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Soil Characteristics ◽  
Machine Learning Techniques ◽  
Chi Square ◽  
Interaction Detection ◽  
Testing Accuracy ◽  
Artificial Neural

To design geotechnical structures efficiently, it is important to examine soil’s physical properties. Therefore, classifying soil with respect to geophysical parameters is an advantageous and popular approach. Novel, quick, cost, and time effective machine learning techniques can facilitate this classification. This study employs three kinds of machine learning models, including the Decision Tree, Artificial Neural Networks, and Bayesian Networks. The Decision tree models included the chi-square automatic interaction detection (CHAID), classification and regression trees (CART), quick, unbiased, and efficient statistical tree (QUEST), and C5; the Artificial Neural Networks models included Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF); and BN models included the Tree Augmented Naïve (TAN) and Markov Blanket, which were employed to predict the soil classifications using geophysics investigations and laboratory tests. The performance of each model was assessed through the accuracy, stability and gains. The results showed that while the BAYESIANMARKOV model achieved the highest overall accuracy (100%) in training phase, this model achieved the lowest accuracy (34.21%) in testing phases. Thus, this model had the worst stability. The QUEST had the second highest overall training accuracy (99.12%) and had the highest overall testing accuracy (94.74%). Thus, this model was somewhat stable and had an acceptable overall training and testing accuracy to predict the soil characteristics. The future studies can use the findings of this paper as a benchmark to classify the soil characteristics and select the best machine learning technique to perform this classification.

Ski injury predictive analytics from massive ski lift transportation data

2017 ◽  
Vol 232 (3) ◽  
pp. 208-217 ◽  
Author(s):  
Boris Delibašić ◽  
Sandro Radovanović ◽  
Miloš Jovanović ◽  
Zoran Obradović ◽  
Milija Suknović
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Predictor Variable ◽  
Transportation System ◽  
Switching Behavior ◽  
Small Scale ◽  
Chi Square ◽  
Response Variable ◽  
Interaction Detection ◽  
Ski Injury

Ski injury research is traditionally studied on small-scale observational studies where risk factors from univariate and multivariate statistical models are extracted. In this article, a large-scale ski injury observational study was conducted by analyzing skier transportation data from six consecutive seasons. Logistic regression and chi-square automatic interaction detection decision tree models for ski injury predictions are proposed. While logistic regression assumes a linearly weighted dependency between the predictors and the response variable, chi-square automatic interaction detection assumes a non-linear and hierarchical dependency. Logistic regression also assumes a monotonic relationship between each predictor variable and the response variable, while chi-square automatic interaction detection does not require such an assumption. In this research, the chi-square automatic interaction detection decision tree model achieved a higher odds ratio and area under the receiver operating characteristic curve in predicting ski injury. Both logistic regression and chi-square automatic interaction detection identified the daily time spent in the ski lift transportation system as the most important feature for ski injury prediction which provides solid evidence that ski injuries are early-failure events. Skiers who are at the highest risk of injury also exhibit higher lift switching behavior while performing faster runs and preferring ski slopes with higher vertical descents. The lowest injury risk is observed for skiers who spend more time in the ski lift transportation system and ski faster than the average population.

scholarly journals THEFT DETECTION IN POWER UTILITIES USING ENSEMBLE OF CHAID DECISION TREE ALGORITHM

2020 ◽  
Vol 2 (2) ◽  
pp. 161-165
Author(s):  
Muhammad Salman Saeed ◽  
Mohd. Wazir Mustafa ◽  
Usman Ullah Sheikh ◽  
Attaullah Khidrani ◽  
Mohd Norzali Haji Mohd
Keyword(s):  
Decision Tree ◽  
Distribution System ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Decision Tree Algorithm ◽  
Chi Square ◽  
Tree Algorithm ◽  
Interaction Detection ◽  
Electricity Theft

Theft of electricity and fraud in energy consumption billing are the primary concerns for Distribution System Operators . Because of those illegal activities, it is believed that billions of dollars are wasted each year. DSOs around the world continue to use conventional time consuming and inefficient methods for non-technical loss detection, particularly in underdeveloped countries . This research work attempts to solve the problems as mentioned above by designing an effective model for detecting electricity theft to classify fraudster customers in a power delivery system. The key motivation for this current study is to support the DSOs in their fight against the stealing of electricity. Initially, the proposed method uses the monthly energy customer consumption data obtained from Multan Electric Power Company (MEPCO) Pakistan to segregate fraudsters and honest customers. The Bagged Chi - square Automatic Interaction Detection (CHAID) based decision tree algorithm is then used to classify the honest and fraudster consumers.Furthermore, based on the mentioned metrics, the performance superiority of the Bagged CHAID-based NTL detection method is validated by comparing its efficacy with that of few well-known state-of-the-art machine learning algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM),) Logistic Regression (LR), Discriminant Analysis and Bayesian Network (BN).

PENERAPAN CHAID DENGAN PENDEKATAN SMOTE PADA KEMATIAN BALITA DI KAWASAN TIMUR INDONESIA TAHUN 2017

2020 ◽  
Vol 2019 (1) ◽  
pp. 357-367
Author(s):  
Isti Samrotul Hidayati ◽  
I Made Arcana
Keyword(s):  
Sampling Technique ◽  
Chi Square ◽  
Interaction Detection ◽  
Chi Squared

Metode Chi-squared Automatic Interaction Detection (CHAID) merupakan metode segmentasi berdasarkan hubungan variabel respon dan penjelas menggunakan uji chi-square, yang dalam penerapannya perlu memperhatikan keseimbangan data untuk meminimalkan kesalahan dalam klasifikasi. Salah satu pendekatan yang dapat digunakan pada data yang tidak seimbang adalah metode Synthetic Minority Over-sampling Technique (SMOTE). Dalam penelitian ini, metode CHAID dengan pendekatan SMOTE diterapkan pada Angka Kematian Balita (AKBa) di Kawasan Timur Indonesia (KTI). Tujuannya adalah untuk mengetahui variabel-variabel yang mencirikan kematian balita berdasarkan metode analisis CHAID yang diterapkan dan membandingkannya dengan pendekatan SMOTE. Hasil perbandingan menunjukkan bahwa pendekatan SMOTE lebih baik digunakan dengan nilai sensitivitas sebesar 48,3% dan nilai presisi sebesar 75,9%. Variabel yang signifikan mencirikan kematian balita di KTI adalah berat badan saat lahir, jenis kelahiran, status bekerja ibu dan kekayaan rumah tangga, dengan karakteristik utama adalah balita yang memiliki berat badan lahir rendah dan terlahir kembar.

scholarly journals Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1094
Author(s):  
Michael Wong ◽  
Nikolaos Thanatsis ◽  
Federica Nardelli ◽  
Tejal Amin ◽  
Davor Jurkovic
Keyword(s):  
Decision Tree ◽  
Expectant Management ◽  
Decision Tree Model ◽  
Decision Tree Analysis ◽  
Focal Lesions ◽  
Tree Model ◽  
Normal Endometrium ◽  
Endometrial Polyps ◽  
Tree Analysis ◽  
Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

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