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NTIS 바로가기지능정보연구 = Journal of intelligence and information systems, v.26 no.1, 2020년, pp.23 - 45
김정훈 (경희대학교 일반대학원 경영학과) , 김민용 (경희대학교 경영대학) , 권오병 (경희대학교 경영대학)
Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorith...
핵심어 | 질문 | 논문에서 추출한 답변 |
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메타특징은 어떤 특성을 반영하는가? | 즉, 데이터셋의 특성에 따라 어떠한 분류알고리즘을 채택하는 것이 적합한지를 판단하는 것은 전문성과 노력이 소요되는 과업이었다. 이는 메타특징(Meta-Feature)으로 불리는 데이터셋의 특성과 판별 알고리즘 성능과의 연관성에 대한 연구가 아직 충분히 이루어지지 않았기 때문이며, 더구나 다중 클래스(Multi-Class)의 특성을 반영하는 메타특징에 대한 연구 또한 거의 이루어진 바 없다. 이에 본 연구의 목적은 다중 클래스 데이터셋의 메타특징이 판별 알고리즘의 성능에 유의한 영향을 미치는지에 대한 실증 분석을 하는 것이다. | |
차원 축소 외에 판별 성능에 영향을 미치는 것은? | (2013)의 연구에서는 판별 알고리즘과 다양한 샘플링 방법을 조합하여 판별 알고리즘 별로 적합한 샘플링 방법이 존재할 수 있다는 것을 밝혔으며, 요인 분석을 통한 차원 축소가 판별 성능을 높이는데 도움이 되지만(Dogan and Tanrikulu, 2013), 과도한 차원의 축소는 오히려 정확도를 떨어뜨리는 결과를 낳기도 한다는 연구도 있다 (Rok and Lusa, 2013). 또한, 클래스의 불균형을 줄일수록 판별 정확도가 높아지기도 한다 (Khoshgoftaar et al., 2010). | |
판별문제란? | 판별문제(Classification)는 데이터 마이닝을 비롯한 지능형 의사결정의 가장 대표적인 분석 문제이며(Chaitra et al., 2018), 판별 알고리즘 간 비교 분석은 의사결정의 질 향상을 위한 오랫동안의 중요한 연구 이슈였다 (Lotte et al. |
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