标签带噪声数据的重加权半监督分类方法
首发时间:2018-12-29
摘要:对于仅有部分数据带标签且标签含有噪声的二分类问题,本文提出了一类基于重要性重加权的半监督分类算法,借助贝叶斯公式和无约束最小二乘拟合进行标签噪声率的估计,并由此利用BP神经网络逐步求解带权的优化问题,在多个标准数据集上的实验结果表明,本文提出重加权的半监督分类方法,能有效地降低标签不足以及标签噪声对分类准确率的影响。
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Reweighting semi-supervised classification for noisy labels
Abstract:This paper proposes a semi-supervised classification algorithm based on importance reweighting for a two-class problem, where only a few data contains noisy labels. The Bayesian formula and unconstrained least squares fitting are used to estimate the noise rate. BP neural network is then used to solve the weighted optimization problem step by step. The experimental results on multiple benchmark sets show that the proposed method can reduce the impact on classification accuracy originated from the label insufficiency and noise.
Keywords: Importance reweighting noise rate semi-supervised classification probability estimation
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