• Journal of Environmental Policy
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• v.9 no.2
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• pp.3-19
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• 2010

Anyang-cheon(stream) runs through southern metropolitan area of Seoul to Han-river in Korea. Due to fast growth of Seoul, the water quality and quantity problems in Anyang-cheon have occurred. To cope with the problems, the Integrated Watershed Management program for Anyang-cheon was adopted and a KRW 26.1 billion (USD 21.8 million) pilot project (construction of 4 facilities such as reservoir) is suggested for 4 sub-watersheds of Anyang-cheon, which cost will be shared by the 12 local governments (LG). Three cost division schemes are compared. By Scheme 1, if the cost is borne by the LG in a watershed where the facilities are constructed (no cost division scheme), the LG in I is to bear 0.58% of the total construction cost, LG in watershed II 29.54%, LG in IV 0%, LG in V 69.88%. In particular, LG in IV in this scheme bears no cost because no facility is constructed, even though watershed IV is the major beneficiary of the facility construction. Scheme 2 is to share the cost by length of streams in each sub-watershed and the suggested cost share for each sub-watershed is 13.76% by I, 7.34% by II, 45.87% by IV, and 33.03% by V. However, this cost division scheme is fair only under the false assumption that the bargaining powers of group of LGs are identical. To suggest a better and fair division rule, Shapley Value, a cooperative game solution, is used to suggest Scheme 3. In Scheme 3, Shapley Value measures the summation of average marginal contribution of each player in all possible coalitions as cost division scheme and is known to provide a fair division considering bargaining power. In the context of Anyang-cheon, LGs in upper stream have superior bargaining position. The result suggests the cost division is fair under Scheme 3, when the cost shares are 0.29% by I, 14.77% by II, 50% by IV, and 34.94% by V, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.15-24
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• 2019

Fair Allocation of profits or costs arising from joint participation by multiple individuals or entities with different purposes is essential for their continuing involvement and for their dissatisfaction reduction. In this research, fair allocation of the profits of forming a grand coalition in Three-Echelon Supply Chain (TESC) game that is composed of manufacturer, distributor and retailer, is studied. In particular, the solutions of the proportional method of profit, the proportional method of marginal profit, and Shapley value based on cooperative game theory are proved to be in the desirable characteristics of the core. The proportional method of profit and the proportional method of marginal profit are often used because of their ease of application. These methods distribute total profit in proportion to profits or marginal profits of each game participant. In addition, Shapley value can be defined as the average marginal profit when one game player is added at a time. Even though the calculation of the average of all possible marginal profits is not simple, Shapley value are often used as a useful method. Experiments have shown that the solution of the incremental method, which calculates the marginal cost of adding game players in the order of manufacturers, distributors and retailers, does not exist in the core.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.53-58
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• 2023

With the fast development of artificial intelligence day by day, users are demanding explanations about the results of algorithms and want to know what parameters influence the results. In this paper, we propose a model for bankruptcy prediction with interpretability using the SHAP framework. SHAP (SHAPley Additive exPlanations) is framework that gives a visualized result that can be used for explanation and interpretation of machine learning models. As a result, we can describe which features are important for the result of our deep learning model. SHAP framework Force plot result gives us top features which are mainly reflecting overall model score. Even though Fully Connected Neural Networks are a "black box" model, Shapley values help us to alleviate the "black box" problem. FCNNs perform well with complex dataset with more than 60 financial ratios. Combined with SHAP framework, we create an effective model with understandable interpretation. Bankruptcy is a rare event, then we avoid imbalanced dataset problem with the help of SMOTE. SMOTE is one of the oversampling technique that resulting synthetic samples are generated for the minority class. It uses K-nearest neighbors algorithm for line connecting method in order to producing examples. We expect our model results assist financial analysts who are interested in forecasting bankruptcy prediction of companies in detail.

• Journal of Korea Water Resources Association
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• v.47 no.7
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• pp.629-641
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• 2014

This study explores methods of allocating costs incurring from construction of environmental infrastructure among local governments involved in the project. Principles for equitable cost-allocation are reviewed, and pros/cons associated with different methods are examined. Proportional Allocation method, Shapley Value method, SCRB (Separable Cost-Remaining Benefits) method are applied to a case of swage treatment plant in Gyeongnam province region, Korea. It is found that the SCRB method produces the most equitable result, followed by Shapley method. The Proportional Allocation method, although easy to understand and simple to calculate, is found to be skewed in favor of small town.

• Journal of Convergence Society for SMB
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• v.3 no.2
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• pp.19-25
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• 2013

Decision procedure was done with the evaluation of multi-criterion analysis. Importance of each criterion was considered through heuristically method, specially it was based on the heuristic least mean square algorithm. To consider coalition evaluation, it was carried out by calculation of Shapley index and Interaction value. The model output is also analyzed with the help of those two indexes, and the procedure was also displayed with details. Finally, the differences between the model output and the desired results are evaluated thoroughly, several problems are raised at the end of the example which require for further studying.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2286-2309
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• 2016

Overlay networks have been widely deployed upon the Internet by Service Providers (SPs) to provide improved network services. However, the interaction between each overlay and traffic engineering (TE) as well as the interaction among co-existing overlays may occur. In this paper, we adopt both non-cooperative and cooperative game theory to analyze these interactions, which are collectively called hybrid interaction. Firstly, we model a situation of the hybrid interaction as an n+1-player non-cooperative game, in which overlays and TE are of equal status, and prove the existence of Nash equilibrium (NE) for this game. Secondly, we model another situation of the hybrid interaction as a 1-leader-n-follower Stackelberg-Nash game, in which TE is the leader and co-existing overlays are followers, and prove that the cost at Stackelberg-Nash equilibrium (SNE) is at least as good as that at NE for TE. Thirdly, we propose a cooperative coalition mechanism based on Shapley value to overcome the inherent inefficiency of NE and SNE, in which players can improve their performance and form stable coalitions. Finally, we apply distinct genetic algorithms (GA) to calculate the values for NE, SNE and the assigned cost for each player in each coalition, respectively. Analytical results are confirmed by the simulation on complex network topologies.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.185-199
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• 2024

Purpose: The improvement of yield and quality in product manufacturing is crucial from the perspective of process management. Controlling key variables within the process is essential for enhancing the quality of the produced items. In this study, we aim to identify key variables influencing product defects and facilitate quality enhancement in CNC machining process using SHAP(SHapley Additive exPlanations) Methods: Firstly, we conduct model training using boosting algorithm-based models such as AdaBoost, GBM, XGBoost, LightGBM, and CatBoost. The CNC machining process data is divided into training data and test data at a ratio 9:1 for model training and test experiments. Subsequently, we select a model with excellent Accuracy and F1-score performance and apply SHAP to extract variables influencing defects in the CNC machining process. Results: By comparing the performances of different models, the selected CatBoost model demonstrated an Accuracy of 97% and an F1-score of 95%. Using Shapley Value, we extract key variables that positively of negatively impact the dependent variable(good/defective product). We identify variables with relatively low importance, suggesting variables that should be prioritized for management. Conclusion: The extraction of key variables using SHAP provides explanatory power distinct from traditional machine learning techniques. This study holds significance in identifying key variables that should be prioritized for management in CNC machining process. It is expected to contribute to enhancing the production quality of the CNC machining process.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5B
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• pp.734-740
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• 2010

In this paper, we propose an effective energy management algorithm for wireless networks. The proposed algorithm sets the multiple routing paths and transmits routing packets based on the cooperative game model. This approach can enhance the network performance under different and diversified network situations. The simulation results demonstrate that the proposed algorithm generally exhibits superior performance compared with the other existing scheme under light to heavy traffic loads.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.58-65
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• 2023

Bankruptcy is a significant risk for start-up companies, but with the help of cutting-edge artificial intelligence technology, we can now predict bankruptcy with detailed explanations. In this paper, we implemented the Category Boosting algorithm following data cleaning and editing using OpenRefine. We further explained our model using the Shapash library, incorporating domain knowledge. By leveraging the 5C's credit domain knowledge, financial analysts in banks or investors can utilize the detailed results provided by our model to enhance their decision-making processes, even without extensive knowledge about AI. This empowers investors to identify potential bankruptcy risks in their business models, enabling them to make necessary improvements or reconsider their ventures before proceeding. As a result, our model serves as a "glass-box" model, allowing end-users to understand which specific financial indicators contribute to the prediction of bankruptcy. This transparency enhances trust and provides valuable insights for decision-makers in mitigating bankruptcy risks.