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Article
Publication date: 19 June 2017

Shang-Yu Chen

Due to such issues as the recent economic recession, low salaries, and an aging society, how people can strengthen their investment performance when managing their personal…

Abstract

Purpose

Due to such issues as the recent economic recession, low salaries, and an aging society, how people can strengthen their investment performance when managing their personal financial affairs is a critical consideration. The purpose of this paper is to consider the assessment of the performance of individual investment policies and to present an evaluation framework for measuring the degree of workability of investment policies.

Design/methodology/approach

The proposed evaluation framework combines the fuzzy analytical hierarchy process and the improved fuzzy technique for order preference by similarity to the ideal solution to measure the efficiency scores of the alternatives (i.e. investment policies) under assessment.

Findings

This quantitative framework is formed from the criteria of investment return, taxation, risk, and individual circumstances according to prudent evaluation of private wealth management research, and is applied to appraise the investment performance of individuals in Taiwan. The findings indicate that investment performance, risks, and the investment of mutual funds are the most preferred conditions and investment policy for investors, and can offer some effective suggestions for investors as well as for future academic research.

Originality/value

The efficiency scores are computed based on the fuzzy Mahalanobis distances, taking into account the fuzzy correlations among experts’ criteria. The advantage of adopting the fuzzy Mahalanobis distances over the fuzzy Euclidean distances, which are typically computed in the literature, is that the undulation of the efficiency scores can be reduced.

Details

Management Decision, vol. 55 no. 5
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 21 August 2002

Chao‐Ton Su and Te‐Sheng Li

The relationships among multi‐dimensional data (such as medical examination data) with ambibuity and variation are difficult to explore. The traditional approach to building a…

97

Abstract

The relationships among multi‐dimensional data (such as medical examination data) with ambibuity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back‐propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi‐dimensional feature selection. The first one proposed is a feature selection procedure from the trained back‐propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis‐Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi’s fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD; it can deal with a reduced model, which is the focus of this paper. In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

Article
Publication date: 4 September 2017

Sagar Sikder, Subhash Chandra Panja and Indrajit Mukherjee

The purpose of this paper is to develop a new easy-to-implement distribution-free integrated multivariate statistical process control (MSPC) approach with an ability to recognize…

Abstract

Purpose

The purpose of this paper is to develop a new easy-to-implement distribution-free integrated multivariate statistical process control (MSPC) approach with an ability to recognize out-of-control points, identify the key influential variable for the out-of-control state, and determine necessary changes to achieve the state of statistical control.

Design/methodology/approach

The proposed approach integrates the control chart technique, the Mahalanobis-Taguchi System concept, the Andrews function plot, and nonlinear optimization for multivariate process control. Mahalanobis distance, Taguchi’s orthogonal array, and the main effect plot concept are used to identify the key influential variable responsible for the out-of-control situation. The Andrews function plot and nonlinear optimization help to identify direction and necessary correction to regain the state of statistical control. Finally, two different real life case studies illustrate the suitability of the approach.

Findings

The case studies illustrate the potential of the proposed integrated multivariate process control approach for easy implementation in varied manufacturing and process industries. In addition, the case studies also reveal that the multivariate out-of-control state is primarily contributed by a single influential variable.

Research limitations/implications

The approach is limited to the situation in which a single influential variable contributes to out-of-control situation. The number and type of cases used are also limited and thus generalization may not be debated. Further research is necessary with varied case situations to refine the approach and prove its extensive applicability.

Practical implications

The proposed approach does not require multivariate normality assumption and thus provides greater flexibility for the industry practitioners. The approach is also easy to implement and requires minimal programming effort. A simple application Microsoft Excel is suitable for online implementation of this approach.

Originality/value

The key steps of the MSPC approach are identifying the out-of-control point, diagnosing the out-of-control point, identifying the “influential” variable responsible for the out-of-control state, and determining the necessary direction and the amount of adjustment required to achieve the state of control. Most of the approaches reported in open literature are focused only until identifying influencing variable, with many restrictive assumptions. This paper addresses all key steps in a single integrated distribution-free approach, which is easy to implement in real time.

Details

International Journal of Quality & Reliability Management, vol. 34 no. 8
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 5 March 2018

Cecilia Guadalupe Mota-Gutiérrez, Edgar Omar Reséndiz-Flores and Yadira Iracema Reyes-Carlos

The purpose of this paper is to show a bibliographical review of the applications of the MTS throughout the time and the different fields.

Abstract

Purpose

The purpose of this paper is to show a bibliographical review of the applications of the MTS throughout the time and the different fields.

Design/methodology/approach

The Mahalanobis-Taguchi system (MTS) is an analytical method used for the diagnosis and/or pattern recognition of multivariate data for quantitative decision making.

Findings

Its scope is very broad, ranging from engineering, medicine, education, and manufacturing, among others. This work presents a classification of the literature in the following areas of the MTS: introduction of the method, cases of study/application, comparison with other methods, integration and development of the MTS with other methods, construction of Mahalanobis space, dimensional reduction and threshold establishment. It realized a wide search of the publications in magazines and congresses.

Originality/value

This paper is a summary of the main applications, contributions and changes to MTS.

Details

International Journal of Quality & Reliability Management, vol. 35 no. 3
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 2 March 2015

Elham Ghasemi, Abdollah Aaghaie and Elizabeth A. Cudney

The purpose of this paper is to present and analyze the current literature related to developing and improving the Mahalanobis-Taguchi system (MTS) and to present the shortcomings…

Abstract

Purpose

The purpose of this paper is to present and analyze the current literature related to developing and improving the Mahalanobis-Taguchi system (MTS) and to present the shortcomings related to this method for future research.

Design/methodology/approach

In this paper, articles in the literature are classified to give an overview on the MT strategy. For this purpose, 46 articles are considered for classification from 2000 to 2013 on the basis of: MTS contribution area, description of the issue, and results.

Findings

In this paper a review on the concepts and operations of the MTS was provided as a new method in the field of pattern recognition, multivariable diagnosis, and forecasting. A large number of studies were performed in recent years consisting of developing MTS and MTS case studies. The analysis of the articles showed the fields of MTS which had more potential for future studies and developing. The comparison of the MTS to other methods and the selection of the normal group for constructing the Mahalanobis space have received the most attention by researchers. In addition, several studies concentrated on the use of other methods instead of design of experiments, finding applications for multiclass MTS and finding an alternative for the SN ratio.

Originality/value

This paper contains the publications in the field of MTS chronologically and shows different areas for developing and case studies. It will be useful to researchers and professionals who are interested in pattern recognition, multivariate analysis, and forecasting.

Details

International Journal of Quality & Reliability Management, vol. 32 no. 3
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 27 May 2014

Boby John

The purpose of this paper is to develop a methodology to reduce the field failures of splined shafts. The paper also demonstrates the application of Mahalanobis-Taguchi system…

Abstract

Purpose

The purpose of this paper is to develop a methodology to reduce the field failures of splined shafts. The paper also demonstrates the application of Mahalanobis-Taguchi system (MTS) for identifying the optimum hardness profile to avoid failures.

Design/methodology/approach

Through the usage profile analysis and comparison between the failed and good shafts, the major reason for shaft failure was identified as hardness variation. Then MTS approach was used to identify the optimum hardness profile for the shafts. An experiment was designed with power, feed and the gap between inductor and quench ring representing the heat transfer rate, heat removal rate and the time between heat transfer and removal of induction hardening process as factors. Based on experimental results, the optimum combination factors that would reduce the variation around the optimum hardness profile were identified.

Findings

The study showed that the shaft failures can be reduced by optimizing the hardness profile of the shafts rather than warning customers on overloading, changing the raw material or investing on machining operation to achieve better shaft finish. The study suggested heat transfer rate, heat removal rate and the time between heat transfer and removal had significant impact on the shaft's hardness profile. The study resulted in reducing the field failures from 0.32 to 0.029 percent.

Practical implications

This study provides valuable information on how to identify optimum hardness profile using MTS methodology to reduce shaft failures and how to minimize the variation around the optimum hardness profile using design of experiments.

Originality/value

To the best of author's knowledge, no study has been conducted to identify optimum hardness profile using MTS methodology. The study also provides an approach to minimize the variation around a non-linear hardness profile using design of experiments.

Details

International Journal of Quality & Reliability Management, vol. 31 no. 6
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 2 November 2020

Sandra García-Bustos, Joseph León and María Nela Pastuizaca

This research proposes a multivariate control chart, whose parameters are optimized using genetic algorithms (GA) in order to accelerate the detection of a change in the vector of…

Abstract

Purpose

This research proposes a multivariate control chart, whose parameters are optimized using genetic algorithms (GA) in order to accelerate the detection of a change in the vector of means.

Design/methodology/approach

This chart is based on a variation of the Hotelling T2 chart using a sampling scheme called generalized multiple dependent state sampling. For the analysis of performances of this chart, the out-of-control average run length (ARL) values were used for different scenarios. In this comparison, it was considered the classic Hotelling T2 chart and the T2 chart using the scheme called multiple dependent state sampling.

Findings

It was observed that the new chart with its optimized parameters is more efficient to detect an out-of-control process. Additionally, a sensitivity analysis was performed, and it was concluded that the best yields are obtained when the change to be considered in the optimization is small. An application in the resolution of a real problem is given.

Originality/value

In this research, a multivariate control chart is proposed based on the Hotelling T2 statistic but adding a sampling scheme. This makes this control chart more efficient than the classic T2 chart because the new chart not only uses the current information of the T2 statistic but also conditions the decision to consider a process as “in- control” on the statistic's previous information. The practitioner can obtain the optimal parameters of this new chart through a friendly program developed by the authors.

Details

International Journal of Quality & Reliability Management, vol. 38 no. 6
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 1 June 2012

Amir H. Meghdadi and James F. Peters

The purpose of this paper is to demonstrate the effectiveness and advantages of using perceptual tolerance neighbourhoods in tolerance space‐based image similarity measures and…

Abstract

Purpose

The purpose of this paper is to demonstrate the effectiveness and advantages of using perceptual tolerance neighbourhoods in tolerance space‐based image similarity measures and its application in content‐based image classification and retrieval.

Design/methodology/approach

The proposed method in this paper is based on a set‐theoretic approach, where an image is viewed as a set of local visual elements. The method also includes a tolerance relation that detects the similarity between pairs of elements, if the difference between corresponding feature vectors is less than a threshold 2 (0,1).

Findings

It is shown that tolerance space‐based methods can be successfully used in a complete content‐based image retrieval (CBIR) system. Also, it is shown that perceptual tolerance neighbourhoods can replace tolerance classes in CBIR, resulting in more accuracy and less computations.

Originality/value

The main contribution of this paper is the introduction of perceptual tolerance neighbourhoods instead of tolerance classes in a new form of the Henry‐Peters tolerance‐based nearness measure (tNM) and a new neighbourhood‐based tolerance‐covering nearness measure (tcNM). Moreover, this paper presents a side – by – side comparison of the tolerance space based methods with other published methods on a test dataset of images.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 5 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 7 December 2020

Zhibin Zhou, Jongwook Kwon, Bo Zhang, Junjian Li, Hak cho Kim and Ji Hyun Heo

During the past several decades, national distance (ND) increasingly became a vital cornerstone in international business (IB) research, as both explicit and implicit distance are…

198

Abstract

Purpose

During the past several decades, national distance (ND) increasingly became a vital cornerstone in international business (IB) research, as both explicit and implicit distance are parts of the essential reasons for IB activities. However, there are various and chaotic methods to measure ND in the last literature; therefore, this paper aims to suggest legitimate uses of ND in the IB field and the best ND dimensions for various situations.

Design/methodology/approach

This paper used a historical overview of the theoretical background and conceptual development of ND based on the past four decades worth of studies in leading 17-IB journals using Google Scholar. The authors also focus on multiform ND measurement methods and details through qualitative and quantitative analysis based on previous studies’ data collection.

Findings

This research summarized the common measurement methods and elements of different ND and proposed solutions based on a multifaceted analysis.

Originality/value

The micro analysis examines each type of ND in terms of the proportion of variables, issues, measurement methods, representative proxies beyond previous studies. This research also tried to provide clarity and suggest solutions to these problems through our macro& micro-analysis.

Details

Review of International Business and Strategy, vol. 31 no. 2
Type: Research Article
ISSN: 2059-6014

Keywords

Article
Publication date: 24 August 2021

Rajakumar Krishnan, Arunkumar Thangavelu, P. Prabhavathy, Devulapalli Sudheer, Deepak Putrevu and Arundhati Misra

Extracting suitable features to represent an image based on its content is a very tedious task. Especially in remote sensing we have high-resolution images with a variety of…

Abstract

Purpose

Extracting suitable features to represent an image based on its content is a very tedious task. Especially in remote sensing we have high-resolution images with a variety of objects on the Earth's surface. Mahalanobis distance metric is used to measure the similarity between query and database images. The low distance obtained image is indexed at the top as high relevant information to the query.

Design/methodology/approach

This paper aims to develop an automatic feature extraction system for remote sensing image data. Haralick texture features based on Contourlet transform are fused with statistical features extracted from the QuadTree (QT) decomposition are developed as feature set to represent the input data. The extracted features will retrieve similar images from the large image datasets using an image-based query through the web-based user interface.

Findings

The developed retrieval system performance has been analyzed using precision and recall and F1 score. The proposed feature vector gives better performance with 0.69 precision for the top 50 relevant retrieved results over other existing multiscale-based feature extraction methods.

Originality/value

The main contribution of this paper is developing a texture feature vector in a multiscale domain by combining the Haralick texture properties in the Contourlet domain and Statistical features using QT decomposition. The features required to represent the image is 207 which is very less dimension compare to other texture methods. The performance shows superior than the other state of art methods.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 14 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

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