• 전자통신동향분석
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• 제34권1호
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• pp.98-110
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• 2019

Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.

• Journal of Information Technology Applications and Management
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• 제30권1호
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• pp.53-70
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• 2023

Artificial intelligence has become familiar with modern society, not the distant future. As artificial intelligence and machine learning developed more highly and became more complicated, it became difficult for people to grasp its structure and the basis for decision-making. It is because machine learning only shows results, not the whole processes. As artificial intelligence developed and became more common, people wanted the explanation which could provide them the trust on artificial intelligence. This study recognized the necessity and importance of explainable artificial intelligence, XAI, and examined the trends of XAI research by analyzing social networks and analyzing topics with IEEE published from 2004, when the concept of artificial intelligence was defined, to 2022. Through social network analysis, the overall pattern of nodes can be found in a large number of documents and the connection between keywords shows the meaning of the relationship structure, and topic modeling can identify more objective topics by extracting keywords from unstructured data and setting topics. Both analysis methods are suitable for trend analysis. As a result of the analysis, it was found that XAI's application is gradually expanding in various fields as well as machine learning and deep learning.

• 한국분말재료학회지
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• 제30권3호
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• pp.210-216
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• 2023

In this study, machine learning models are proposed to predict the Vickers hardness of AlSi10Mg alloys fabricated by laser powder bed fusion (LPBF). A total of 113 utilizable datasets were collected from the literature. The hyperparameters of the machine-learning models were adjusted to select an accurate predictive model. The random forest regression (RFR) model showed the best performance compared to support vector regression, artificial neural networks, and k-nearest neighbors. The variable importance and prediction mechanisms of the RFR were discussed by Shapley additive explanation (SHAP). Aging time had the greatest influence on the Vickers hardness, followed by solution time, solution temperature, layer thickness, scan speed, power, aging temperature, average particle size, and hatching distance. Detailed prediction mechanisms for RFR are analyzed using SHAP dependence plots.

• 한국전자거래학회지
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• 제26권3호
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• pp.97-117
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• 2021

정기예금 가입 여부 예측은 은행의 대표적인 금융 마케팅 중 하나로, 은행은 다양한 고객 정보를 활용하여 예측 모델을 구성할 수 있다. 정기예금 가입 여부의 분류 정확도를 향상하기 위해, 많은 연구에서 기계학습 기법들을 이용하여 분류 모델들을 개발하였다. 하지만, 이러한 모델들이 만족스러운 성능을 보일지라도 모델의 의사결정 과정에 대한 근거가 적절하게 설명되지 않는다면 산업에서 활용하기가 쉽지 않다. 이러한 문제점을 해결하기 위해, 본 논문은 설명 가능한 정기예금 가입 여부 예측 기법을 제안한다. 먼저, 테이블 형식에서 우수한 성능을 도출하는 의사결정 나무 기반 앙상블 학습 기법인 랜덤 포레스트, GBM, XGBoost, LightGBM을 이용하여 분류 모델들을 개발하고, 10겹 교차검증을 통해 모델들의 분류 성능을 심층 분석한다. 다음으로, 가장 우수한 성능을 도출하는 모델에 설명 가능한 인공지능 기법인 SHAP을 적용하여 고객 정보의 영향도와 의사결정 과정 등을 해석할 수 있는 근거를 제공한다. 제안한 기법의 실용성과 타당성을 입증하기 위해, Kaggle에서 제공한 은행 마케팅 데이터 셋을 대상으로 모의실험을 진행하였으며, 데이터 셋 구성에 따라 GBM과 LightGBM 모델에 SHAP을 각기 적용하여 설명 가능한 정기예금 가입 여부를 위한 분석 및 시각화를 수행하였다.

• 인터넷정보학회논문지
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• 제23권2호
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• pp.97-105
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• 2022

Web services show a rapid evolution and integration to meet the increased users' requirements. Thus, web services undergo updates and may have performance degradation due to undetected faults in the updated versions. Due to these faults, many performances and regression anomalies in web services may occur in real-world scenarios. This paper proposed applying the deep learning model and innovative explainable framework to detect performance and regression anomalies in web services. This study indicated that upper bound and lower bound values in performance metrics provide us with the simple means to detect the performance and regression anomalies in updated versions of web services. The explainable deep learning method enabled us to decide the precise use of deep learning to detect performance and anomalies in web services. The evaluation results of the proposed approach showed us the detection of unusual behavior of web service. The proposed approach is efficient and straightforward in detecting regression anomalies in web services compared with the existing approaches.

• 한국정보전자통신기술학회논문지
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• 제17권3호
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• pp.176-182
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• 2024

본 연구는 결측치 비율이 높은 시계열 데이터를 효과적으로 분석하고 예측할 수 있는 머신러닝 모델을 구축하기 위해 다양한 결측치 처리 방법을 비교 분석하였다. 이를 위해 PSMF(Predictive State Model Filtering), MissForest, IBFI(Imputation By Feature Importance) 방법을 적용하였으며, 이후 LightGBM, XGBoost, EBM(Explainable Boosting Machines) 머신러닝 모델을 사용하여 예측 성능을 평가하였다. 연구 결과, 결측치 처리 방법 중에서는 MissForest와 IBFI가 비선형적 데이터 패턴을 잘 반영하여 가장 높은 성능을 나타냈으며, 머신러닝 모델 중에서는 XGBoost와 EBM 모델이 LightGBM 모델보다 더 높은 성능을 보였다. 본 연구는 결측치 비율이 높은 시계열 데이터의 분석 및 예측에 있어 비선형적 결측치 처리 방법과 머신러닝 모델의 조합이 중요함을 강조하며, 실무적으로 유용한 방법론을 제시하였다.

• 인터넷정보학회논문지
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• 제25권4호
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• 전자통신동향분석
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• 제38권6호
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• pp.95-106
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• 2023

As artificial intelligence has become commonplace in various fields, the transparency of AI in its development and implementation has become an important issue. In safety-critical areas, the eXplainable and/or understandable of artificial intelligence is being actively studied. On the other hand, machine learning have been applied to the intelligence of self-organizing network (SON), but transparency in this application has been neglected, despite the critical decision-makings in the operation of mobile communication systems. We describes concepts of eXplainable machine learning (ML), along with research trends, major issues, and research directions. After summarizing the ML research on SON, research directions are analyzed for explainable ML required in intelligent SON of beyond 5G and 6G communication.

• 과학교육연구지
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• 제47권1호
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• pp.37-51
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• 2023

본 연구는 사범대 과학교육전공 재학생의 전공만족도와 학업만족도에 영향을 주는 요인을 머신러닝 모델인 랜덤 포레스트와 그래디언트 부스팅 모델과 SHAP 기법을 활용하여 탐색했다. 연구 결과, 그래디언트 부스팅 모델의 성능이 랜덤 포레스트보다 우수한 것으로 드러났으나 그 차이는 크지 않았다. 전공만족도에 영향을 주는 요인으로는 '본인 전공 교과에 해당하는 고교시절 과학교사 만족도', '교직 동기', '나이' 등이 있으며, 학업만족도는 '나이', '성별', '내신 과학 전문교과 이수여부'의 영향을 크게 받는 것으로 드러났다. SHAP value를 활용하여 변인의 영향력을 밝힐 수 있었고, 그 결과가 집단 전체에 대한 것과 개별적 분석으로 각각 도출이 가능했고, 서로 보완적 결과가 도출이 가능함을 확인하였다. 연구 결과를 바탕으로 과학교육과 재학생의 전공 및 학업 만족도를 지원하기 위한 방안을 제안하였다.

• 융합신호처리학회논문지
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• 제24권4호
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• pp.186-191
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• 2023

데이터 처리 기술은 의사결정을 위해 중요한 역할을 하며, 데이터 결측값 및 이상값 처리, 예측, 추천 모델 등이 포함 된다. 이는 모든 과정과 결과의 타당성, 신뢰성, 정확성에 대한 명확한 설명이 필요하다. 또한 의사결정트리, 추론 등을 이용한 설명가능한 모델을 통해 데이터의 문제를 해결하고, 다양한 유형의 학습을 고려하여 모델 경량화를 진행할 필요가 있다. 육하원칙을 적용한 다중 계층 마이닝 분류 방법은 데이터 전처리 후 트랜잭션에서 빈번하게 발생하는 변수와 속성 간의 다차원 관계를 발견하는 방법이다. 이는 트랜잭션에서 마이닝을 이용하여 유의미한 관계를 발견하고, 회귀분석을 통해 데이터를 모델링 하는 방법을 설명한다. 이에따라 확장 가능한 모델과 로지스틱 회귀모델을 개발하고, 데이터 정제, 관련성 분석, 데이터 변환, 데이터 증강을 통해 클래스 레이블을 생성하여 설명가능한 의사결정을 위한 미이닝 기술을 제안한다.