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Open Access
Article
Publication date: 22 November 2022

Kedong Yin, Yun Cao, Shiwei Zhou and Xinman Lv

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index…

Abstract

Purpose

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Design/methodology/approach

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Findings

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

Originality/value

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Details

Marine Economics and Management, vol. 5 no. 2
Type: Research Article
ISSN: 2516-158X

Keywords

Article
Publication date: 16 April 2020

Mohammad Mahdi Ershadi and Abbas Seifi

This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification

Abstract

Purpose

This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods based on data, experts’ knowledge and both are considered in some cases. Besides, feature reduction and some clustering methods are used to improve their performance.

Design/methodology/approach

First, the performances of classification methods are evaluated for differential diagnosis of different diseases. Then, experts' knowledge is utilized to modify the Bayesian networks' structures. Analyses of the results show that using experts' knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification. A total of ten different diseases are used for testing, taken from the Machine Learning Repository datasets of the University of California at Irvine (UCI).

Findings

The proposed method improves both the computation time and accuracy of the classification methods used in this paper. Bayesian networks based on experts' knowledge achieve a maximum average accuracy of 87 percent, with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods.

Practical implications

The proposed methodology can be applied to perform disease differential diagnosis analysis.

Originality/value

This study presents the usefulness of experts' knowledge in the diagnosis while proposing an adopted improvement method for classifications. Besides, the Bayesian network based on experts' knowledge is useful for different diseases neglected by previous papers.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Book part
Publication date: 6 September 2019

Son Nguyen, Gao Niu, John Quinn, Alan Olinsky, Jonathan Ormsbee, Richard M. Smith and James Bishop

In recent years, the problem of classification with imbalanced data has been growing in popularity in the data-mining and machine-learning communities due to the emergence…

Abstract

In recent years, the problem of classification with imbalanced data has been growing in popularity in the data-mining and machine-learning communities due to the emergence of an abundance of imbalanced data in many fields. In this chapter, we compare the performance of six classification methods on an imbalanced dataset under the influence of four resampling techniques. These classification methods are the random forest, the support vector machine, logistic regression, k-nearest neighbor (KNN), the decision tree, and AdaBoost. Our study has shown that all of the classification methods have difficulty when working with the imbalanced data, with the KNN performing the worst, detecting only 27.4% of the minority class. However, with the help of resampling techniques, all of the classification methods experience improvement on overall performances. In particular, the Random Forest, in combination with the random over-sampling technique, performs the best, achieving 82.8% balanced accuracy (the average of the true-positive rate and true-negative rate).

We then propose a new procedure to resample the data. Our method is based on the idea of eliminating “easy” majority observations before under-sampling them. It has further improved the balanced accuracy of the Random Forest to 83.7%, making it the best approach for the imbalanced data.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78754-290-7

Keywords

Article
Publication date: 31 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.

Details

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

Keywords

Article
Publication date: 17 August 2018

Youlong Lv, Wei Qin, Jungang Yang and Jie Zhang

Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements…

Abstract

Purpose

Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.

Design/methodology/approach

The decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.

Findings

The proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.

Practical implications

This research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.

Originality/value

The adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.

Details

Industrial Management & Data Systems, vol. 118 no. 8
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 1 April 2006

Janita F.J. Vos and Marjolein C. Achterkamp

The management of stakeholder involvement within innovation projects is a task of growing importance. The purpose of this paper is to present a method for the first…

7366

Abstract

Purpose

The management of stakeholder involvement within innovation projects is a task of growing importance. The purpose of this paper is to present a method for the first challenge in stakeholder management: the identification of those stakeholders to be involved in innovation projects.

Design/methodology/approach

Analysis of stakeholder literature leads to the conclusion that stakeholder identification is considered a problem of classification. Although the availability of a classification model is necessary, it is argued that for a classification model to be of use in identifying stakeholders, such a model needs to be supplemented with an identification procedure for identifying real world parties. Furthermore, a classification model should fit the context the stakeholders are identified for, in this case for innovation projects. These insights have led to the development of a classification model fitting the innovation context, and to the embedding of this model, along with a matching identification procedure, in an identification method.

Findings

A partial and integral evaluation of the method on four cases showed its efficacy in the managerial practice of identifying stakeholders within innovation projects.

Originality/value

The method as proposed in the paper can be used for identifying stakeholders in innovation projects. The method can be considered a first step in managing stakeholder involvement.

Details

European Journal of Innovation Management, vol. 9 no. 2
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 25 February 2021

Baohua Yang, Junming Jiang and Jinshuai Zhao

The purpose of this study is to construct a gray relational model based on information diffusion to avoid rank reversal when the available decision information is…

Abstract

Purpose

The purpose of this study is to construct a gray relational model based on information diffusion to avoid rank reversal when the available decision information is insufficient, or the decision objects vary.

Design/methodology/approach

Considering that the sample dependence of the ideal sequence selection in gray relational decision-making is based on case sampling, which causes the phenomenon of rank reversal, this study designs an ideal point diffusion method based on the development trend and distribution skewness of the sample information. In this method, a gray relational model for sample classification is constructed using a virtual-ideal sequence. Subsequently, an optimization model is established to obtain the criteria weights and classification radius values that minimize the deviation between the comprehensive relational degree of the classification object and the critical value.

Findings

The rank-reversal problem in gray relational models could drive decision-makers away from using this method. The results of this study demonstrate that the proposed gray relational model based on information diffusion and virtual-ideal sequencing can effectively avoid rank reversal. The method is applied to classify 31 brownfield redevelopment projects based on available interval gray information. The case analysis verifies the rationality and feasibility of the model.

Originality/value

This study proposes a robust method for ideal point choice when the decision information is limited or dynamic. This method can reduce the influence of ideal sequence changes in gray relational models on decision-making results considerably better than other approaches.

Details

Grey Systems: Theory and Application, vol. 12 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 14 August 2019

Karen Scarlette Sanhueza and Christopher Nikulin

The purpose of this paper is to address the emerging need to map knowledge and information with a novel classification, suitable to have a clear and integrated overview of…

Abstract

Purpose

The purpose of this paper is to address the emerging need to map knowledge and information with a novel classification, suitable to have a clear and integrated overview of the design method, models and techniques from both the sides of product and process. The proposed classification allows to understand main relevance of different design methods, models and techniques according their characteristic and also level in where company usually applied.

Design/methodology/approach

The authors decided to structure the research into three steps: from the analysis of background literature, in order to draw the main evidences for the development of a novel classification, to their application. First, the papers search related to collect the different methods used in literature. Second, paper characterization which aims to understand main traits and usefulness of design methods, models and tools. Third, the assessment of design methods, models and tools according proposed classification.

Findings

Each method, model or technique would be more useful according to the context in which is applied. Most of methods and modes can be continuously improving, considering different sub-classification or complement each other, striving to compensate to the extent possible for weakness in any one of the approaches.

Research limitations/implications

The proposed classification did not deliver absolute results in every analyzed model or techniques, it delivered a wide range of possibilities in every sub-classification, thus the engineers get multiple options to choose depending on its main goal or the available resources.

Originality/value

The author’s proposal aims at filling a classification gap in the design method literature, which has to plausible in use. The different alternatives can be represented according to a scalable and hierarchical logic embedding also a more structured evaluation of the methods and tools in practice.

Details

Business Process Management Journal, vol. 25 no. 7
Type: Research Article
ISSN: 1463-7154

Keywords

Article
Publication date: 3 December 2020

Erik Bergström, Fredrik Karlsson and Rose-Mharie Åhlfeldt

The purpose of this paper is to develop a method for information classification. The proposed method draws on established standards, such as the ISO/IEC 27002 and…

Abstract

Purpose

The purpose of this paper is to develop a method for information classification. The proposed method draws on established standards, such as the ISO/IEC 27002 and information classification practices. The long-term goal of the method is to decrease the subjective judgement in the implementation of information classification in organisations, which can lead to information security breaches because the information is under- or over-classified.

Design/methodology/approach

The results are based on a design science research approach, implemented as five iterations spanning the years 2013 to 2019.

Findings

The paper presents a method for information classification and the design principles underpinning the method. The empirical demonstration shows that senior and novice information security managers perceive the method as a useful tool for classifying information assets in an organisation.

Research limitations/implications

Existing research has, to a limited extent, provided extensive advice on how to approach information classification in organisations systematically. The method presented in this paper can act as a starting point for further research in this area, aiming at decreasing subjectivity in the information classification process. Additional research is needed to fully validate the proposed method for information classification and its potential to reduce the subjective judgement.

Practical implications

The research contributes to practice by offering a method for information classification. It provides a hands-on-tool for how to implement an information classification process. Besides, this research proves that it is possible to devise a method to support information classification. This is important, because, even if an organisation chooses not to adopt the proposed method, the very fact that this method has proved useful should encourage any similar endeavour.

Originality/value

The proposed method offers a detailed and well-elaborated tool for information classification. The method is generic and adaptable, depending on organisational needs.

Details

Information & Computer Security, vol. 29 no. 2
Type: Research Article
ISSN: 2056-4961

Keywords

Article
Publication date: 16 September 2021

Sireesha Jasti

Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the…

Abstract

Purpose

Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not.

Design/methodology/approach

A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity.

Findings

By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms.

Originality/value

The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted.

Details

International Journal of Web Information Systems, vol. 17 no. 6
Type: Research Article
ISSN: 1744-0084

Keywords

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