Integrated artificial intelligence-based resizing strategy and multiple criteria decision making technique to form a management decision in an imbalanced environment

Lin, Sin-Jin

doi:10.1007/s13042-016-0574-3

Cited by 13 publications

(4 citation statements)

References 49 publications

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“…The average prediction error it provides for oil industry is around 6.30, and for power at 7.87. Both ANN, SVR are not sensitive to data distribution, and have a strong application ability [30]. Out of our expectation, the two ensemble models, AdaBoost and RF doesn't provide accuracy prediction results.…”

Section: B Robust Check Of Classifiersmentioning

confidence: 84%

A Contrastive Study of Machine Learning on Energy Firm Value Prediction

Zhang

Liu

2020

IEEE Access

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For decades, a high prediction error rate of firm value assessment has been reported by using traditional financial evaluation methods, therefore develop a suitable assessment tool to improve firm value prediction accuracy is in urgent. This paper provides a comprehensive review and statistical comparison of six machine learning models: K-Nearest Neighbor, Decision Trees, Support Vector Regression, Artificial Neutral Network, AdaBoost, and Random Forest in oil firm and power firm value prediction. Based on nearly 5000 M&A items, this paper finds that for both oil and power industries, the prediction error of ANN is the lowest in all the three measurement terms. ANN performs better than the other five ML models by 18% at least for oil industry, and outperforms the others by 19% for power industry. It shows that ANN models can produce both accurate and reasonably understandable prediction results. ANN can be applied to a wide range of M&A decisions and value assessment for energy firms.

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Section: B Robust Check Of Classifiersmentioning

confidence: 84%

A Contrastive Study of Machine Learning on Energy Firm Value Prediction

Zhang

Liu

2020

IEEE Access

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“…The corporate credit rating status is evaluated by an independent institution that aims to find out how a corporation is able to meet its financial obligation and specifically relies on a detailed and comprehensive analysis of all the risk factors of the measure object [41]. The usage of credit rating status is widely taken as a measure of corporation's risk and creditworthiness [42,43]. The rating status can be divided into 10 ranks, ranging from best to worst (ranks from 1 to 4 express low risk; from 5 to 6 express middle risk; and from 7 to 10 express high To ensure that the selected performance measure was fairly representative, we considered each corporation's credit rating status.…”

Section: Robustness Testmentioning

confidence: 99%

A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development

Zhang

Lee

2019

Sustainability

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Measuring financial performance has become an essential topic due to the potential decimating impacts on the corporation itself as well as to whole societies during financial turmoil. In order to provide an overarching description of the multidimensional nature for measuring a corporation’s operations, it is preferable to employ data envelopment analysis (DEA). Different from prior research that merely focuses on a singular DEA performance rank, this study extends it to multiple DEA specifications (i.e., it combines inputs and outputs in several different ways) so as to make judgments more complete and robust. We also execute fuzzy visualization technique (i.e., nonlinear fuzzy robust principal component analysis, NFRPCA) to represent the main characteristics of data so that non-specialists can have better access to the results. The analyzed result is then fed into the restricted Boltzmann machine (RBM) to establish a model to forecast a firm’s operating performance. Even a fraction of accuracy improvement can result in considerable future savings to a firm and investors. When examined using real cases, the model is a promising alternative for operating performance forecasting and can assist both internal and external market participants.

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“…Furthermore, AI technology can be used to analyze big data, by collecting and summarizing data from multiple sources, providing auditors with sufficient evidence and information that can be included in judgments, supplementing their judgment capability, and making more informed decisions to provide clients with higher levels of assurance. More importantly, AI-enabled auditing techniques are prominent in data analysis, such as data extraction, comparison, and validation (Hsu & Lin, 2016;Lin, 2017), which means that AI-based technology can extract textual information from complex electronic documents (Deloitte, 2015). Thus, auditors can spend more time in areas that require higher levels of judgment and present greater insights to businesses.…”

Section: Introductionmentioning

confidence: 99%

Identifying Key Factors for Adopting Artificial Intelligence-Enabled Auditing Techniques by Joint Utilization of Fuzzy-Rough Set Theory and MRDM Technique

Chen

Hsu

et al. 2020

Technological and Economic Development of Economy

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In today’s big-data era, enterprises are able to generate complex and non-structured information that could cause considerable challenges for CPA firms in data analysis and to issue improper audited reports within the required period. Artificial intelligence (AI)-enabled auditing technology not only facilitates accurate and comprehensive auditing for CPA firms, but is also a major breakthrough in auditing’s new environment. Applications of an AI-enabled auditing technique in external auditing can add to auditing efficiency, increase financial reporting accountability, ensure audit quality, and assist decision-makers in making reliable decisions. Strategies related to the adoption of an AI-enabled auditing technique by CPA firms cover the classical multiple criteria decision-making (MCDM) task (i.e., several perspectives/criteria must be considered). To address this critical task, the present study proposes a fusion multiple rule-based decision making (MRDM) model that integrates rule-based technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) into MCDM techniques that can assist decision makers in selecting the best methods necessary to achieve the aspired goals of audit success. We also consider potential implications for articulating suitable strategies that can improve the adoption of AI-enabled auditing techniques and that target continuous improvement and sustainable development.

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Integrated artificial intelligence-based resizing strategy and multiple criteria decision making technique to form a management decision in an imbalanced environment

Cited by 13 publications

References 49 publications

A Contrastive Study of Machine Learning on Energy Firm Value Prediction

A Contrastive Study of Machine Learning on Energy Firm Value Prediction

A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development

Identifying Key Factors for Adopting Artificial Intelligence-Enabled Auditing Techniques by Joint Utilization of Fuzzy-Rough Set Theory and MRDM Technique

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