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Article
Publication date: 1 October 1995

Susan Coles and Jennifer Rowley

Explores, using appropriate examples, the ways in which decisiontrees can be used by the manager to assist in the longitudinaldecision‐making process. Since the…

Abstract

Explores, using appropriate examples, the ways in which decision trees can be used by the manager to assist in the longitudinal decision‐making process. Since the mathematical concepts associated with decision trees are complex, managers can be reluctant to attempt to use decision tree models. A recognition that such models can be simply developed in a spreadsheet environment, and can then be used for sensitivity analysis using different decision criteria, demonstrates that decision trees can offer valuable insights into the structure of a strategic decision problem.

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Management Decision, vol. 33 no. 8
Type: Research Article
ISSN: 0025-1747

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Book part
Publication date: 31 January 2015

Davy Janssens and Geert Wets

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. In our application…

Abstract

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. In our application, we will use decision rules to support the decision-making of the model instead of principles of utility maximization, which means our work can be interpreted as an application of the concept of bounded rationality in the transportation domain. In this chapter we explored a novel idea of combining decision trees and Bayesian networks to improve decision-making in order to maintain the potential advantages of both techniques. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of a travel demand model with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.

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Bounded Rational Choice Behaviour: Applications in Transport
Type: Book
ISBN: 978-1-78441-071-1

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Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

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Article
Publication date: 24 July 2019

Zhe Zhang and Yue Dai

For classification problems of customer relationship management (CRM), the purpose of this paper is to propose a method with interpretability of the classification results…

Abstract

Purpose

For classification problems of customer relationship management (CRM), the purpose of this paper is to propose a method with interpretability of the classification results that combines multiple decision trees based on a genetic algorithm.

Design/methodology/approach

In the proposed method, multiple decision trees are combined in parallel. Subsequently, a genetic algorithm is used to optimize the weight matrix in the combination algorithm.

Findings

The method is applied to customer credit rating assessment and customer response behavior pattern recognition. The results demonstrate that compared to a single decision tree, the proposed combination method improves the predictive accuracy and optimizes the classification rules, while maintaining interpretability of the classification results.

Originality/value

The findings of this study contribute to research methodologies in CRM. It specifically focuses on a new method with interpretability by combining multiple decision trees based on genetic algorithms for customer classification.

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Asia Pacific Journal of Marketing and Logistics, vol. 32 no. 5
Type: Research Article
ISSN: 1355-5855

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Article
Publication date: 13 August 2018

Shrawan Kumar Trivedi and Prabin Kumar Panigrahi

Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification…

Abstract

Purpose

Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy but also by sensitivity (correctly classified legitimate emails) and specificity (correctly classified unsolicited emails) towards the accurate classification, captured by both false positive and false negative rates. This paper aims to present a comparative study between various decision tree classifiers (such as AD tree, decision stump and REP tree) with/without different boosting algorithms (bagging, boosting with re-sample and AdaBoost).

Design/methodology/approach

Artificial intelligence and text mining approaches have been incorporated in this study. Each decision tree classifier in this study is tested on informative words/features selected from the two publically available data sets (SpamAssassin and LingSpam) using a greedy step-wise feature search method.

Findings

Outcomes of this study show that without boosting, the REP tree provides high performance accuracy with the AD tree ranking as the second-best performer. Decision stump is found to be the under-performing classifier of this study. However, with boosting, the combination of REP tree and AdaBoost compares favourably with other classification models. If the metrics false positive rate and performance accuracy are taken together, AD tree and REP tree with AdaBoost were both found to carry out an effective classification task. Greedy stepwise has proven its worth in this study by selecting a subset of valuable features to identify the correct class of emails.

Research limitations/implications

This research is focussed on the classification of those email spams that are written in the English language only. The proposed models work with content (words/features) of email data that is mostly found in the body of the mail. Image spam has not been included in this study. Other messages such as short message service or multi-media messaging service were not included in this study.

Practical implications

In this research, a boosted decision tree approach has been proposed and used to classify email spam and ham files; this is found to be a highly effective approach in comparison with other state-of-the-art modes used in other studies. This classifier may be tested for different applications and may provide new insights for developers and researchers.

Originality/value

A comparison of decision tree classifiers with/without ensemble has been presented for spam classification.

Details

Journal of Systems and Information Technology, vol. 20 no. 3
Type: Research Article
ISSN: 1328-7265

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Article
Publication date: 20 March 2007

Jun‐Geol Baek

Condition‐based maintenance (CBM) has increasingly drawn attention in industry because of its many benefits. The CBM problem is a kind of state‐dependent scheduling…

Abstract

Purpose

Condition‐based maintenance (CBM) has increasingly drawn attention in industry because of its many benefits. The CBM problem is a kind of state‐dependent scheduling problem, and is very hard to solve within the conventional Markov decision process framework. The purpose of this paper is to present an intelligent CBM scheduling model for which incremental decision tree learning as an evolutionary system identification model and dynamic programming as a control model are developed.

Design/methodology/approach

To fully exploit the merits of CBM, this paper models CBM scheduling as a state‐dependent, sequential decision‐making problem. The objective function is formulated as the minimization of the total maintenance cost. Instead of interpreting the problem within the widely used Markovian framework, this paper proposes an intelligent maintenance scheduling approach that integrates an incremental decision tree learning method and deterministic dynamic programming techniques.

Findings

Although the intelligent maintenance scheduling approach proposed in this paper does not guarantee an optimal scheduling policy from a mathematical viewpoint, it is verified through a simulation‐based experiment that the intelligent maintenance scheduler is capable of providing a good scheduling policy that can be used in practice.

Originality/value

This paper presents an intelligent maintenance scheduler. As a system identification model, we devise a new incremental decision tree learning method by which interaction patterns among attributes and machine condition are disclosed in an evolutionary manner. A deterministic dynamic programming technique is then applied to select the best safe state in terms of the total maintenance cost.

Details

International Journal of Quality & Reliability Management, vol. 24 no. 3
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 1 October 2006

Leisheng Peng, Duminda Wijesekera, Thomas C. Wingfield and James B. Michael

This paper aims to assist investigators and attorneys addressing the legal aspects of cyber incidents, and allow them to determine the legality of a response to cyber…

Abstract

Purpose

This paper aims to assist investigators and attorneys addressing the legal aspects of cyber incidents, and allow them to determine the legality of a response to cyber attacks by using the Worldwide web securely.

Design/methodology/approach

Develop a decision support legal whiteboard that graphically constructs legal arguments as a decision tree. The tree is constructed using a tree of questions and appending legal documents to substantiate the answers that are known to hold in anticipated legal challenges.

Findings

The tool allows participating group of attorneys to meet in cyberspace in real time and construct a legal argument graphically by using a decision tree. They can construct sub‐parts of the tree from their own legal domains. Because diverse legal domains use different nomenclatures, this tool provides the user the capability to index and search legal documents using a complex international legal ontology that goes beyond the traditional LexisNexis‐like legal databases. This ontology itself can be created using the tool from distributed locations.

Originality/value

This tool has been fine‐tuned through numerous interviews with attorneys teaching and practicing in the area of cyber crime, cyber espionage, and military operations in cyberspace. It can be used to guide forensic experts and law enforcement personnel during their active responses and off‐line examinations.

Details

Internet Research, vol. 16 no. 5
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 26 July 2011

Erick T. Byrd and Larry Gustke

The purpose of this paper is to investigate the use of decision tree analysis in the identification of stakeholders based on their participation in tourism and political…

Abstract

Purpose

The purpose of this paper is to investigate the use of decision tree analysis in the identification of stakeholders based on their participation in tourism and political activities in a community.

Design/methodology/approach

A survey was sent to tourism stakeholders in two rural counties. Responses were collected and analyzed using the exhaustive chi‐square automatic interaction detection decision tree analysis.

Findings

Based on the results of the decision tree analysis four tourism stakeholder groups were identified based on their participation in tourism and political activities in a community: high participants, high‐moderate participants, low‐moderate participants, and low participants.

Research limitations/implications

Owing to a low response rate, an issue of non‐response bias could exist, but the information from the respondents can give insight on stakeholders in these communities. Also, the specific results of this study can only be applied to eastern North Carolina and are not generalizable to other areas.

Practical implications

Results from this study demonstrate the use of decision tree analysis in identifying community stakeholders. Using decision tree analysis tourism planners can identify stakeholder groups that will participate in tourism and political activities. With this knowledge, tourism planners can identify which stakeholder groups will be the most influential and vocal in a community with regard to tourism development.

Originality/value

Decision tree analysis is a tool for partitioning a data set based on the relationships between a set of independent variables and a dependent variable. The research reported here tests the application of decision tree analysis, an analytical technique that is not traditionally used to segment stakeholders in tourism.

Details

Journal of Place Management and Development, vol. 4 no. 2
Type: Research Article
ISSN: 1753-8335

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Article
Publication date: 6 June 2008

Norbert Tóth and Béla Pataki

The purpose of this paper is to provide classification confidence value to every individual sample classified by decision trees and use this value to combine the classifiers.

Abstract

Purpose

The purpose of this paper is to provide classification confidence value to every individual sample classified by decision trees and use this value to combine the classifiers.

Design/methodology/approach

The proposed system is first theoretically explained, and then the use and effectiveness of the proposed system is demonstrated on sample datasets.

Findings

In this paper, a novel method is proposed to combine decision tree classifiers using calculated classification confidence values. This confidence in the classification is based on distance calculation to the relevant decision boundary (distance conditional), probability density estimation and (distance conditional) classification confidence estimation. It is shown that these values – provided by individual classification trees – can be integrated to derive a consensus decision.

Research limitations/implications

The proposed method is not limited to axis‐parallel trees, it is applicable not only to oblique trees, but also to any kind of classifier system that uses hyperplanes to cluster the input space.

Originality/value

A novel method is presented to extend decision tree like classifiers with confidence calculation and a voting system is proposed that uses this confidence information. The proposed system possesses several novelties (e.g. it not only gives class probabilities, but also classification confidences) and advantages over previous (traditional) approaches. The voting system does not require an auxiliary combiner or gating network, as in the mixture of experts structure and the method is not limited to decision trees with axis‐parallel splits; it is applicable to any kind of classifiers that use hyperplanes to cluster the input space.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 1 no. 2
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 20 February 2009

Che‐Chern Lin, Hung‐Jen Yang and Lung‐Hsing Kuo

The purpose of this paper is to explore teachers' behaviours in completing an internet survey using decision trees. Furthermore, to reduce the complexity of the decision

Abstract

Purpose

The purpose of this paper is to explore teachers' behaviours in completing an internet survey using decision trees. Furthermore, to reduce the complexity of the decision trees, a statistical technique was used to decrease the number of input variables in the decision trees.

Design/methodology/approach

A dataset of 47,647 samples was used to build the decision trees. These samples were collected from an internet survey of teachers in Taiwan. The output of the decision trees was the answering time (the time taken to complete the internet questionnaire). Eight variables were selected as the inputs for the decision trees. Two techniques were employed to build the decision trees – the exhaustive chi‐squared automatic interaction detector (ECHAID) and classification and regression tree (CRT) analysis. To reduce the complexity of the decision models, factor analysis technique was used to decrease the data dimensions (number of input variables) and to obtain a simplified decision model. One‐way ANOVA was used to validate the effects of the dimension reduction.

Findings

From the results of the factor analysis, a simplified decision tree is recommended using four input variables – teaching years, school level, sex and area. The classification accuracy of the simplified model is statistically equivalent to that of the original one, which used eight input variables.

Originality/value

The complexity of decision trees theoretically depends on the number of input variables. This study used a statistical technique to decrease the number of input variables and thereby reduce the complexity of the decision trees. A statistical technique was employed to validate that the classification accuracy is not statistically different between the original decision model and the simplified one. The decision models proposed in this paper can be applied in estimating the answering time for completing a questionnaire during an internet survey.

Details

Online Information Review, vol. 33 no. 1
Type: Research Article
ISSN: 1468-4527

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