A decision tree model for the prediction of the stay time of ships in Brazilian ports

Abreu, Levi Ribeiro de; Maciel, Ingrid S.F.; Alves, Joab S.; Braga, Lucas C.; Pontes, Heráclito Lopes Jaguaribe

doi:10.1016/j.engappai.2022.105634

Cited by 13 publications

(2 citation statements)

References 40 publications

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“…(3) Machine learning techniques: Many studies have switched to machine learning techniques [22][23][24] to overcome the limitations of statistical assumptions [25] and successfully solve various prediction (or classification) problems in the real world. More common classification techniques used, such as rough set theory (RST) [26], DT [27], RF [28], and MLP [29,30], have become an important research trend at present. Moreover, Bayes network (BN) learning [31], logistic regression (LR) [32], naïve Bayesian (NB) [33], and support vector machine (SVM) [34] classifiers are always emerging techniques helpful for industry application fields; thus, they were also selected and emphasized in this study for the sake of comparison.…”

Section: Continuous Research Motivation and Research Originalitymentioning

confidence: 99%

“…A DT is a special tree structure that utilizes a tree-like graph and possible outcomes to model a target decision [57]. DTs [27] are commonly used algorithms, including CARTs (classification and regression trees), ID3 (Iterative Dichomizer 3), and C4.5 rule induction algorithms. (1) The CART algorithm was proposed by Breiman et al [58]; it is a binary DT, which is composed of nodes formed at different stages and branches between nodes under various conditions.…”

Section: Research On Decision Tree Learning Classifier and Its Relate...mentioning

confidence: 99%

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A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

Chen

Hung

Lin³

2023

Mathematics

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With the motivation of long-term care 2.0 plans, forecasting models to identify potential customers of long-term care insurance (LTCI) are an important and interesting issue. From the limited literature, most past researchers emphasize traditional statistics techniques to address this issue; however, these are lacking in some areas. For example, intelligent hybrid models for LTCI are lacking, performance measurement of components for hybrid models is lacking, and research results for interpretative capacities are lacking, resulting in a black box scenario and difficulty in making decisions, and the gap between identifying potential customers and constructing hybrid models is unbridged. To solve the shortcomings mentioned above, this study proposes some advanced intelligent single and hybrid models; the study object is LTCI customers. The proposed hybrid models were used on the experimental dataset collected from real insurance data and possess the following advantages: (1) The feature selection technique was used to simplify variables for the purpose of improving model performance. (2) The performance of hybrid models was evaluated against some machine learning methods, including rough set theory, decision trees, multilayer perceptron, support vector machine, genetic algorithm, random forest, logistic regression, and naive Bayes, and sensitivity analysis was performed in terms of accuracy, coverage, rules number, and standard deviation. (3) We used the C4.5 algorithm of decision trees and the LEM2 algorithm of rough sets to extract and provide valuably comprehensible decisional rules as decision-making references for the interested parties for their varied benefits. (4) We used post hoc testing to verify the significant difference in groups. Conclusively, this study effectively identifies potential customers for their key attributes and creates a decision rule set of knowledge for use as a reference when solving practical problems by forming a structured solution. This study is a new trial in the LTCI application field and realizes novel creative application values. Such a hybrid model is rarely seen in identifying LTCI potential customers; thus, the study has sufficient application contribution and managerial benefits to attract much concern from the interested parties.

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Section: Continuous Research Motivation and Research Originalitymentioning

confidence: 99%

Section: Research On Decision Tree Learning Classifier and Its Relate...mentioning

confidence: 99%

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

Chen

Hung

Lin³

2023

Mathematics

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A comprehensive evaluation of ensemble machine learning in geotechnical stability analysis and explainability

Lin,

Liang,

Zhao

et al. 2023

Int J Mech Mater Des

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A two-stage fuzzy inference model to determine raw materials criticality in life sciences industries

Abreu

Nagano

2023

Oper Manag Res

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A decision tree model for the prediction of the stay time of ships in Brazilian ports

Cited by 13 publications

References 40 publications

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

A comprehensive evaluation of ensemble machine learning in geotechnical stability analysis and explainability

A two-stage fuzzy inference model to determine raw materials criticality in life sciences industries

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