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结合加权KNN和自适应牛顿法的稳健Boosting方法

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结合加权KNN和自适应牛顿法的稳健Boosting方法
Robust Boosting Method Combining Weighted KNN and Adaptive Newton Method
投稿时间：2019-06-19
DOI：10.15918/j.tbit1001-0645.2019.174
中文关键词:AdaBoost算法噪声先验概率加权KNN损失函数自适应牛顿法
English Keywords:AdaBoost algorithmnoise prior probabilityweighted KNNloss functionadaptive Newton method
基金项目:国家"十三五"科技支撑计划项目（SQ2018YFC200004）
作者单位E-mail
罗森林北京理工大学 信息与电子学院, 北京 100081
赵惟肖北京理工大学 信息与电子学院, 北京 100081
潘丽敏北京理工大学 信息与电子学院, 北京 100081panlimin@bit.edu.cn
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中文摘要:
Boosting是机器学习领域中重要的集成学习方法，以AdaBoost为代表的Boosting算法通过在组合弱学习器时不断加强对错分类样本的关注以构建性能优异的强学习器，而该训练机制对噪声点的无差别对待易引发学习器对噪声过拟合，从而削弱算法的稳健性.针对该问题，提出结合加权KNN和自适应牛顿法的稳健Boosting方法.该方法首先通过加权KNN估计样本的噪声先验概率，然后使用噪声先验概率修正Logit损失构建一种新的损失函数，最后采用自适应牛顿法进行损失函数的优化求解.提出方法引导分类器在给予错分类样本更高权重的同时，对噪声先验概率大的样本给予相应的惩罚，使噪声样本的权重得到有效的缩减.结果表明，与其他稳健Boosting方法对比，在不同噪声水平下以及真实的医疗数据集的不同评价指标下，该方法表现出更好的稳健性，具有明显的应用价值.
English Summary:
Boosting is an essential ensemble learning method in the field of machine learning. continuously strengthening the attention of misclassified samples when combined with weak learners. Strengthening the attention continuously to misclassified samples with weak learners,the Boosting algorithms represented by AdaBoost are capable of building strong learners with excellent performance. However, there is an indiscriminate treatment of noise in the training mechanism, causing the learners likely to over-fit the noise and thus reducing the robustness of the algorithms. Aiming at the problem, a robust Boosting method combining weighted KNN and adaptive Newton method was proposed. Firstly, a weighted KNN method was used to estimate the noise prior probability of the sample. And then, the Logit loss was modified with the noise prior probability to construct a new loss function. Finally, the loss function was optimized based on an adaptive Newton method. The proposed method was arranged to give a corresponding penalty to the samples with a high probability of noise when the misclassified samples got a higher weight from the classifier, so as to make the weight of the noise samples be effectively reduced. The experiment results show that, compared with other robust Boosting methods, the proposed method has better robustness under different noise levels as well as under different evaluation criterions in a real medical data set, having obvious application value.
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