CAJ | 학술논문

随机森林是一种集成分类器，对影响随机森林性能的参数进行了分析，结果表明随机森林中树的数量对随机森林的性能影响至关重要。对树的数量的确定方法以及随机森林性能指标的评价方法进行了研究与总结。以分类精度为评价方法，利用UCI数据集对随机森林中决策树的数量与数据集的关系进行了实验分析，实验结果表明对于多数数据集，当树的数量为100时，就可以使分类精度达到要求。将随机森林和分类性能优越的支持向量机在精度方面进行了对比，实验结果表明随机森林的分类性能可以与支持向量机相媲美。
수궤삼림시일충집성분류기，대영향수궤삼림성능적삼수진행료분석，결과표명수궤삼림중수적수량대수궤삼림적성능영향지관중요。대수적수량적학정방법이급수궤삼림성능지표적평개방법진행료연구여총결。이분류정도위평개방법，이용UCI수거집대수궤삼림중결책수적수량여수거집적관계진행료실험분석，실험결과표명대우다수수거집，당수적수량위100시，취가이사분류정도체도요구。장수궤삼림화분류성능우월적지지향량궤재정도방면진행료대비，실험결과표명수궤삼림적분류성능가이여지지향량궤상비미。
Random Forest(RF)is a kind of ensemble classifier. This paper analyses the parameters influencing the per-formance of RF, and the result shows that the number of trees in random forest has significant effect on its performance. This paper carries on a research and summary on the method of determining the number of trees and evaluating the perfor-mance index of RF, with the classification accuracy used as the evaluation method, utilizing UCI data sets, an experimental analysis on the relationship between the number of decision trees in random forest and the data sets has been done. The experimental result shows that for the majority of data sets, when the number of trees is 100, the classification accuracy can meet the requirement. This paper compares RF with support vector machine having superior classification perfor-mance in the aspect of accuracy, and the result shows that the classification performance of random forest is similar to that of support vector machine.