模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2014年
2期
187-192
,共6页
机器学习%类别不平衡学习%欠采样%集成
機器學習%類彆不平衡學習%欠採樣%集成
궤기학습%유별불평형학습%흠채양%집성
Machine Learning%Class-Imbalance Learning%Under-Sampling%Ensemble
随机欠采样方法忽略潜在有用的大类样本信息,在面对多类分类问题时更为突出.文中提出多类类别不平衡学习算法：EasyEnsemble. M.该算法通过多次针对大类样本随机采样,充分利用被随机欠采样方法忽略的潜在有用的大类样本,学习多个子分类器,利用混合的集成技术最终得到性能较优的强分类器.实验结果表明,与常用的多类类别不平衡学习算法相比,EasyEnsemble. M可有效提高分类器的G-mean值.
隨機欠採樣方法忽略潛在有用的大類樣本信息,在麵對多類分類問題時更為突齣.文中提齣多類類彆不平衡學習算法：EasyEnsemble. M.該算法通過多次針對大類樣本隨機採樣,充分利用被隨機欠採樣方法忽略的潛在有用的大類樣本,學習多箇子分類器,利用混閤的集成技術最終得到性能較優的彊分類器.實驗結果錶明,與常用的多類類彆不平衡學習算法相比,EasyEnsemble. M可有效提高分類器的G-mean值.
수궤흠채양방법홀략잠재유용적대류양본신식,재면대다류분류문제시경위돌출.문중제출다류유별불평형학습산법：EasyEnsemble. M.해산법통과다차침대대류양본수궤채양,충분이용피수궤흠채양방법홀략적잠재유용적대류양본,학습다개자분류기,이용혼합적집성기술최종득도성능교우적강분류기.실험결과표명,여상용적다류유별불평형학습산법상비,EasyEnsemble. M가유효제고분류기적G-mean치.
The potential useful information in the majority class is ignored by stochastic under-sampling. When under-sampling is applied to multi-class imbalance problem, this situation becomes even worse. In this paper, EasyEnsemble. M for multi-class imbalance problem is proposed. The potential useful information contained in the majority classes which is ignored is explored by stochastic sampling the majority classes for multiple times. Then, sub-classifiers are learned and a strong classifier is obtained by using hybrid ensemble techniques. Experimental results show that EasyEnsemble. M is superior to other frequently used multi-class imbalance learning methods when G-mean is used as performance measure.
