• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.182-190
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• 2023

This study proposes a predictive model using XGBoost and SHapley Additive exPlanation (SHAP) to estimate fuel consumption in bulk carriers. Previous studies have also utilized ship engine data and weather data. However, they lacked reliability in predicted results and explanations of variables used in the fuel consumption prediction model implementation. To address these limitations, this study developed a predictive model using XGBoost and SHAP. It provides research background, scope, relevant regulations, previous studies, and research methodology. Additionally, it explains the data cleaning method for bulk carriers and verifies results of the predictive model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.177-184
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• 2024

In the situation of increasing data at fast speed, we use all kinds of complex ensemble and deep learning algorithms to get the highest accuracy. It's sometimes questionable how these models predict, classify, recognize, and track unknown data. Accomplishing this technique and more has been and would be the goal of intensive research and development in the data science community. A variety of reasons, such as lack of data, imbalanced data, biased data can impact the decision rendered by the learning models. Many models are gaining traction for such interpretations. Now, LIME and SHAP are commonly used, in which are two state of the art open source explainable techniques. However, their outputs represent some different results. In this context, this study introduces a coupling technique of LIME and Shap, and demonstrates analysis possibilities on the decisions made by LightGBM and Keras models in classifying a transaction for fraudulence on the IEEE CIS dataset.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Convergence Security Journal
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• v.22 no.3
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• pp.101-110
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• 2022

As the cases of using artificial intelligence in various fields increase, attempts to solve various issues through artificial intelligence in the intrusion detection field are also increasing. However, the black box basis, which cannot explain or trace the reasons for the predicted results through machine learning, presents difficulties for security professionals who must use it. To solve this problem, research on explainable AI(XAI), which helps interpret and understand decisions in machine learning, is increasing in various fields. Therefore, in this paper, we propose an explanatory AI to enhance the reliability of machine learning-based intrusion detection prediction results. First, the intrusion detection model is implemented through XGBoost, and the description of the model is implemented using SHAP. And it provides reliability for security experts to make decisions by comparing and analyzing the existing feature importance and the results using SHAP. For this experiment, PKDD2007 dataset was used, and the association between existing feature importance and SHAP Value was analyzed, and it was verified that SHAP-based explainable AI was valid to give security experts the reliability of the prediction results of intrusion detection models.

• Journal of Science Education
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• v.47 no.1
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• pp.37-51
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• 2023

This study explored the factors influencing major satisfaction and academic satisfaction of science education major students at the College of Education using machine learning models, random forest, gradient boosting model, and SHAP. Analysis results showed that the performance of the gradient boosting model was better than that of the random forest, but the difference was not large. Factors influencing major satisfaction include 'satisfaction with science teachers in high school corresponding to the subject of one's major', 'motivation for teaching job', and 'age'. Through the SHAP value, the influence of variables was identified, and the results were derived for the group as a whole and for individual analysis. The comprehensive and individual results could be complementary with each other. Based on the research results, implications for ways to support pre-service science teachers' major and academic satisfaction were proposed.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.768-771
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• 2024

인간의 산업 활동으로 인하여 동물들의 생존이 위협받으면서, 동물의 서식 분포를 효과적으로 파악할 수 있는 자동 야생동물 모니터링 기술의 필요성이 점점 더 커지고 있다. 그중에서도 동물 소리 분류 기술은 시각적으로 식별이 어려운 동물에게도 효과적으로 적용할 수 있는 장점으로 인하여 널리 사용되고 있다. 최근 심층학습 기반의 분류 모델들이 좋은 판별 성능을 보여주고 있어 동물 소리 분류에 많이 사용되고 있지만, 희귀종과 같이 개체 수가 적어 데이터가 부족한 경우에는 학습이 제대로 이루어지지 않을 수 있다. 또한, 이러한 모델들은 모델 내부에서 일어나는 추론 과정을 알 수 없어 결과를 완전히 신뢰하고 사용하는 데 제약이 따른다. 이에 본 논문에서는 전이 학습을 통해 데이터 부족 문제를 고려하고, SHAP을 이용하여 분류 모델의 추론 과정을 해석하는 설명가능한 동물 소리 분류 기법을 제안한다. 실험 결과, 제안하는 기법은 지도 학습을 한 경우보다 분류 성능이 향상됨을 확인하였으며, SHAP 분석을 통해 모델의 분류 근거를 이해할 수 있었다.

• The Journal of Bigdata
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• v.5 no.2
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• pp.215-229
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• 2020

The interest in machine learning is growing in all industries, but it is difficult to apply it to real-world tasks because of inexplicability. This paper introduces a case of developing a financial customer churn prediction model for a securities company, and introduces the research results on an attempt to develop a machine learning model that can be explained using the SHAP Value methodology and derivation of interpretability. In this study, a total of six customer churn models are compared and analyzed, and the cause of customer churn is inferred through the classification and data analysis of SHAP Value and the type of customer asset change. Based on the results of this study, it would be possible to use it as a basis for comprehensive judgment, such as using the Value of the deviation prediction result that can infer the cause of the marketing manager's actual customer marketing in the future and establishing a target marketing strategy for each customer.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1331-1336
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• 2023

In text-based fish disease classification using machine learning, there is a problem that the input parameters of the machine learning model are too many, but due to performance problems, the input parameters cannot be arbitrarily reduced. This paper proposes a method of optimizing input parameters specialized for Paralichthys olivaceus disease classification using SHAP analysis techniques to solve this problem,. The proposed method includes data preprocessing of disease information extracted from the halibut disease questionnaire by applying the SHAP analysis technique and evaluating a machine learning model using AutoML. Through this, the performance of the input parameters of AutoML is evaluated and the optimal input parameter combination is derived. In this study, the proposed method is expected to be able to maintain the existing performance while reducing the number of input parameters required, which will contribute to enhancing the efficiency and practicality of text-based Paralichthys olivaceus disease classification.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.1-11
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• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.845-848
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• 2021

최근 화석연료의 급격한 사용에 따른 자원고갈이나 환경오염과 같은 문제들이 심각해짐에 따라 화석연료를 대체할 수 있는 신재생에너지에 대한 관심이 높아지고 있다. 태양광 에너지는 다른 에너지원에 비해 고갈의 우려가 없고, 부지 선정의 제약이 크지 않아 수요가 증가하고 있다. 태양광 발전 시스템에서 생산된 전력을 효과적으로 사용하기 위해서는 태양광 발전량에 대한 정확한 예측 모델이 필요하다. 이를 위한 다양한 딥러닝 기반의 예측 모델들이 제안되었지만, 이러한 모델들은 모델 내부에서 일어나는 의사결정 과정을 들여다보기가 어렵다. 의사결정에 대한 설명이 없다면 예측 모델의 결과를 완전히 신뢰하고 사용하는 데 제약이 따른다. 이런 문제를 위해서 최근 주목을 받는 설명 가능한 인공지능 기술을 사용한다면, 예측 모델의 결과 도출에 대한 해석을 제공할 수 있어 모델의 신뢰성을 확보할 수 있을 뿐만 아니라 모델의 성능 향상을 기대할 수도 있다. 이에 본 논문에서는 Long Short-Term Memory(LSTM)을 사용하여 모델을 구성하고, 모델에서 어떻게 예측값이 도출되었는지를 SHapley Additive exPlanation(SHAP)을 통하여 설명하는 태양광 발전량 예측 기법을 제안한다.