This page has only limited features, please log in for full access.

Dr. Ming-Fu Hsu
English Program of Global Business, Chinese Culture University, Taipei 11114, Taiwan

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Data Envelopment Analysis
0 Performance Analysis
0 Intelligent system
0 Data/text mining
0 Multiple criteria deacision making

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 04 August 2021 in Axioms
Reads 0
Downloads 0

Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.

ACS Style

Hsueh-Li Huang; Sin-Jin Lin; Ming-Fu Hsu. An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages. Axioms 2021, 10, 179 .

AMA Style

Hsueh-Li Huang, Sin-Jin Lin, Ming-Fu Hsu. An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages. Axioms. 2021; 10 (3):179.

Chicago/Turabian Style

Hsueh-Li Huang; Sin-Jin Lin; Ming-Fu Hsu. 2021. "An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages." Axioms 10, no. 3: 179.

Original article
Published: 12 May 2021 in International Journal of Machine Learning and Cybernetics
Reads 0
Downloads 0

This research introduces a fusion architecture that integrates balanced scorecards (BSCs) and network data envelopment analysis (NDEA) to conduct a performance evaluation task from multiple perspectives. The architecture is able to capture the dynamics of production processes and sub-processes, uncover some of the components behind successful business practices, and shed light on needed actions for decision makers. Furthermore, the architecture not only can support decision makers to plan for improvement, but also equip them with forecasting ability. To enhance its forecasting quality, this study goes beyond quantitative ratios and extends them to qualitative ratios (i.e., readability: the complexities of disclosure) borrowed from computational linguistics. The results indicate that a poor readability score is highly associated with bad operations. Finally, to enlarge the mechanism’s applicable fields, the study executes the genetic algorithm (GA) to extract the inherent decision logics and represents them in a human-readable manner. The mechanism, examined by real cases, is a promising alternative for performance evaluation and forecasting.

ACS Style

Ming-Fu Hsu; Sin-Jin Lin. A BSC-based network DEA model equipped with computational linguistics for performance assessment and improvement. International Journal of Machine Learning and Cybernetics 2021, 12, 2479 -2497.

AMA Style

Ming-Fu Hsu, Sin-Jin Lin. A BSC-based network DEA model equipped with computational linguistics for performance assessment and improvement. International Journal of Machine Learning and Cybernetics. 2021; 12 (9):2479-2497.

Chicago/Turabian Style

Ming-Fu Hsu; Sin-Jin Lin. 2021. "A BSC-based network DEA model equipped with computational linguistics for performance assessment and improvement." International Journal of Machine Learning and Cybernetics 12, no. 9: 2479-2497.

Journal article
Published: 16 April 2021 in Axioms
Reads 0
Downloads 0

Under the ravages of COVID-19, global supply chains have encountered unprecedented disruptions. Past experiences cannot fully explain the situations nor provide any suitable responses to these fatal shocks on supply chain management (SCM), especially in todays’ highly intertwined/globalized business environment. This research thus revisits and rechecks the crucial components for global SCM during such special periods, and the basic essence of such management covers numerous perspectives that can be categorized into a multiple criteria decision making (MCDM) approach. To handle this complex issue appropriately, one can introduce a fusion intelligent system that involves data envelopment analysis (DEA), rough set theory (RST), and MCDM to understand the reality of the analyzed problem in a faster and better manner. Based on the empirical results, we rank the priorities in order as cash management and information (D), raw material supply (B), global management strategy (C), and productivity and logistics (A) for improvement in SCM. This finding is confirmed by companies now undergoing a downsizing strategy in order to survive in this harsh business environment.

ACS Style

Kuang-Hua Hu; Fu-Hsiang Chen; Ming-Fu Hsu; Shuyi Yao; Ming-Chin Hung. Identification of the Critical Factors for Global Supply Chain Management under the COVID-19 Outbreak via a Fusion Intelligent Decision Support System. Axioms 2021, 10, 61 .

AMA Style

Kuang-Hua Hu, Fu-Hsiang Chen, Ming-Fu Hsu, Shuyi Yao, Ming-Chin Hung. Identification of the Critical Factors for Global Supply Chain Management under the COVID-19 Outbreak via a Fusion Intelligent Decision Support System. Axioms. 2021; 10 (2):61.

Chicago/Turabian Style

Kuang-Hua Hu; Fu-Hsiang Chen; Ming-Fu Hsu; Shuyi Yao; Ming-Chin Hung. 2021. "Identification of the Critical Factors for Global Supply Chain Management under the COVID-19 Outbreak via a Fusion Intelligent Decision Support System." Axioms 10, no. 2: 61.

Journal article
Published: 15 May 2018 in Sustainability
Reads 0
Downloads 0

It is widely recognized that a firm’s well-established corporate governance (CG) has a considerable impact on its corporate social responsibility (CSR) performance. How to determine the main trigger among CG’s indicators for strengthening CSR performance is thus an urgent and complicated task due to its (i.e., CSR) multi-dimensional and numerous perspectives. In order to solve this critical problem, the study breaks down CSR into four dimensions and further examines the impact of CG’s indicators on each CSR dimension by joint utilization of rough set theory (RST) and decision tree (DT). By doing so, users can realize which one CG indicator is the most essential to CSR performance. Managers can take the results as a reference to allocate valuable and scarce resources to the right place so as to enhance CSR performance in the future. To solidify our research finding, we transform the CSR forecasting model selection into a multiple criteria decision making (MCDM) task and execute a MCDM algorithm. By implementing the MCDM algorithm, users can achieve a much more reliable and consensus decision in today’s highly turbulent economic environment. The proposed mechanism, examined by real cases, is a promising alternative for CSR performance forecasting.

ACS Style

Kuang-Hua Hu; Sin-Jin Lin; Ming-Fu Hsu. A Fusion Approach for Exploring the Key Factors of Corporate Governance on Corporate Social Responsibility Performance. Sustainability 2018, 10, 1582 .

AMA Style

Kuang-Hua Hu, Sin-Jin Lin, Ming-Fu Hsu. A Fusion Approach for Exploring the Key Factors of Corporate Governance on Corporate Social Responsibility Performance. Sustainability. 2018; 10 (5):1582.

Chicago/Turabian Style

Kuang-Hua Hu; Sin-Jin Lin; Ming-Fu Hsu. 2018. "A Fusion Approach for Exploring the Key Factors of Corporate Governance on Corporate Social Responsibility Performance." Sustainability 10, no. 5: 1582.

Journal article
Published: 12 May 2016 in International Journal of Machine Learning and Cybernetics
Reads 0
Downloads 0

The deterioration in enterprises’ profitability not only threatens the interests of those firms, but also means related parties (investors, bankers, and stakeholders) could encounter tremendous financial losses, which could also impact the circulation of limited economic resources. Thus, an enterprise risk forecasting mechanism is urgently needed to assist decision-makers in adjusting their operating strategies so as to survive under any highly turbulent economic climate. This research introduces a novel hybrid model that incorporates an incremental filter-wrapper feature subset selection with the statistical examination and twin support vector machine (IFWTSVM) for enterprise operating performance forecasting. To promote a hybrid model’s real-life application, the knowledge visualization extracted from IFWTSVM is represented in an easy-to-grasp style. The experimental results reveal that IFWTSVM’s forecasting quality is very promising for financial risk mining, relative to other forecasting techniques examined in this study.

ACS Style

Te-Min Chang; Ming-Fu Hsu. Integration of incremental filter-wrapper selection strategy with artificial intelligence for enterprise risk management. International Journal of Machine Learning and Cybernetics 2016, 9, 477 -489.

AMA Style

Te-Min Chang, Ming-Fu Hsu. Integration of incremental filter-wrapper selection strategy with artificial intelligence for enterprise risk management. International Journal of Machine Learning and Cybernetics. 2016; 9 (3):477-489.

Chicago/Turabian Style

Te-Min Chang; Ming-Fu Hsu. 2016. "Integration of incremental filter-wrapper selection strategy with artificial intelligence for enterprise risk management." International Journal of Machine Learning and Cybernetics 9, no. 3: 477-489.