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Feature selection based on rough set theory using feature space decomposition for mixed-type data
Kyung-Jun Kim(김경준),Chi-Hyuck Jun(전치혁) 한국경영과학회 2016 한국경영과학회 학술대회논문집 Vol.2016 No.4
Feature selection plays an important role in classification problems dealing with mixed-type data. The main idea of feature selection is to reduce the dimensionality of the input space while preserving the classification accuracy by selecting the most important input features. The rough set theory can be an appropriate way of measuring the importance of features in a classification problem, as seen in recent studies. Previous papers related to feature selection based on the rough set theory also considered property of mixed-type data, however, they failed to investigate the properties of numerical and categorical features. To overcome the limitation, we suggest a concept of feature space decomposition. In addition, for fair measure between numerical and categorical feature, we use Heterogeneous Euclideanoverlap Metric (HEOM). Finally, we conduct and show experimental results to compare our proposed method with several benchmarking methods and select the appropriate features through the forward selection algorithm.
Feature selection based on rough set theory using feature space decomposition for mixed-type data
Kyung-Jun Kim(김경준),Chi-Hyuck Jun(전치혁) 대한산업공학회 2016 대한산업공학회 춘계학술대회논문집 Vol.2016 No.4
Feature selection plays an important role in classification problems dealing with mixed-type data. The main idea of feature selection is to reduce the dimensionality of the input space while preserving the classification accuracy by selecting the most important input features. The rough set theory can be an appropriate way of measuring the importance of features in a classification problem, as seen in recent studies. Previous papers related to feature selection based on the rough set theory also considered property of mixed-type data, however, they failed to investigate the properties of numerical and categorical features. To overcome the limitation, we suggest a concept of feature space decomposition. In addition, for fair measure between numerical and categorical feature, we use Heterogeneous Euclideanoverlap Metric (HEOM). Finally, we conduct and show experimental results to compare our proposed method with several benchmarking methods and select the appropriate features through the forward selection algorithm.
표본코호트기반 고지혈증 약제의 저밀도 콜레스테롤 감소량 및 투약순응도 분석
김규진,전치혁,이혜선,김헌성,Kim, Kyu-Jin,Jun, Chi-Hyuck,Lee, Hyeseon,Kim, Hun-Sung 한국데이터정보과학회 2017 한국데이터정보과학회지 Vol.28 No.5
Hyperlipidemia, the status of blood with high level of low-density lipoprotein cholesterol (LDL-C), is known as a main cause of coronary artery diseases such as myocardiac infarction or brain infarct. Statin is the representative prescription to hyperlipidemia and the effects of it depend on the patient's individual conditions such as health-caring habits or adherence to medication. The main effect of statin is reducing LDL-C, which should reach the target range based on National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) guideline. In this research, the reduction of LDL-C and attainment to patient's target range are considered effects of statin. The association between factors - individual conditions and adherence to medication of patients - and the effects of statin is analyzed with National Health Insurance Service-National Sample Cohort (NHIS-NSC). 고지혈증은 혈액 중에 지방 성분이 필요이상으로 많은 상태를 의미하며, 특히 저밀도 콜레스테롤이 혈관벽에 달라붙어 심근경색증, 뇌경색 등의 다양한 심혈관계의 질병을 발생시킬 수 있다. 스타틴은 대표적인 고지혈증 처방제로서 처방 받는 환자의 개별적인 특성 및 건강관리형태, 투약순응도 등에 따라 그 효과가 달라진다. 스타틴의 주요 효과는 저밀도 콜레스테롤 수치를 낮추는 것인데, 이는 National cholesterol education program-adult treatment panel (NCEP-ATP III) 가이드라인에서 환자의 조건에 따라 정한 목표 수치에 도달해야 한다. 본 연구에서는 저밀도 콜레스테롤 수치의 감소량과 환자 별 목표 수치 도달여부를 각각 스타틴의 효과로 상정하고, 국민건강보험공단에서 구축한 표본코호트 DB를 이용하여 건강검진기록의 개별특성 (나이, 성별, 흡연, 운동 및 혈액검사결과)과 처방전 기록으로부터 투약순응도를 통합해서 저밀도 콜레스테롤 감소량 및 목표도달률에 미치는 영향을 분석하고자 한다.