APPLICATION OF ENSEMBLE ALGORITHM INTEGRATING MULTIPLE CRITERIA FEATURE SELECTION IN CORONARY HEART DISEASE DETECTION
作者： Cai-Jie Qin , Qiang Guan , Xin-Pei Wang
DOI: 10.4015/S1016237217500430
关键词: Coronary heart disease 、 Novelty 、 Data diversity 、 Statistical classification 、 Feature selection 、 Multiple criteria 、 Computer science 、 Algorithm
摘要: Conventional coronary heart disease (CHD) detection methods are expensive, rely much on doctors’ subjective experience, and some of them have side effects. In order to obtain rapid, high-precision, low-cost, non-invasive results, several in machine learning were attempted for CHD this paper. The paper adopted multiple evaluation criteria measure features, combined with heuristic search strategy seven common classification algorithms verify the validity importance feature selection (FS) Z-Alizadeh Sani dataset. On basis, a novelty algorithm integrating FS into ensemble (ensemble based selection, EA-MFS) was further proposed. Bagging approach increase data diversity, used aforementioned MFS functional perturbation, employed major voting method carry out decision performed selective integration terms difference ...
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