【作者】 潘世瑞； 张阳； 李雪； 王勇；

【Author】 PAN Shirui~1,ZHANG Yang~(1,2+),LI Xue~3,WANG Yong~4 1.College of Information Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China 2.State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210093,China 3.School of Information Technology and Electrical Engineering,University of Queensland,Brisbane 4072,Australia 4.School of Computer,Northwestern Polytechnical University,Xi’an 710072,China

【机构】 西北农林科技大学信息工程学院； 南京大学计算机软件新技术国家重点实验室； 昆士兰大学计算机及电子工程系； 西北工业大学计算机学院；

【摘要】 研究了在正例和未标记样本场景下不确定样本的分类问题,提出了一种新的算法NNPU(nearestneighbor algorithm for positive and unlabeled learning)。NNPU具有两种实现方式:NNPUa和NNPUu。在UCI标准数据集上的实验结果表明,充分考虑数据不确定信息的NNPUu算法要比仅仅考虑样本中不确定信息均值的NNPUa算法具有更好的分类能力;同时,NNPU算法在对精确数据进行分类时,比NN-d、OCC以及aPUNB算法性能更优。更多还原

【Abstract】 This paper studies the problem of uncertain data classification under positive and unlabeled(PU) learning scenario.It proposes a novel algorithm,NNPU(nearest neighbor algorithm for positive and unlabeled learning),to handle this problem with two varieties,NNPUa and NNPUu.Experimental results on benchmark UCI datasets show that NNPUu,which considers the whole uncertain information on the datasets,has a better ability to classify unseen examples than NNPUa that considers the average value of uncertainty only.Furthermore,NNPU outperforms some existing algorithms such as NN-d,OCC(one-class classifier) and aPUNB in handling precise data.更多还原