Parallel Cooperative Ensemble Learning by Adaptive Data Weighting and Error-Correcting Output Codes
Parallel Cooperative Ensemble Learning by Adaptive Data Weighting and Error-Correcting Output Codes
复制标题
通过自适应数据加权和纠错输出代码进行并行协作集成学习
DOI：
10.1007/978-3-030-04182-3_59
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Shota Utsumi and Keisuke Kameyama
中科院分区：
文献类型：
--
作者：
後田俊紀;矢内浩文;横川 璃沙，土屋 誠司，渡部 広一;Shota Utsumi and Keisuke Kameyama
AdaBoost使用分配给样本的权重，使最新的弱假设适应现有弱假设的分类错误。然而，AdaBoost对离群值非常敏感，现有的假设无法进一步训练以与新的假设合作。我们提出了一种新的算法，该算法从训练开始就准备好所有弱假设，并并行训练所有弱假设。因此，弱假设能够在训练期间彼此合作。同时，我们改变了样本权值的更新函数，以抑制离群值权值的影响。我们比较了新算法在几种纠错输出码和弱假设类型下的性能。实验结果表明，PCEL算法在大多数数据集上都能提高多类分类的准确率。
AdaBoost uses the weights assigned to samples to make the latest weak hypothesis adapt to classification mistakes of existing weak hypotheses. However, AdaBoost is very sensitive to the outliers and the existing hypotheses cannot be further trained to cooperate with the newer one. We proposed a new algorithm which prepares all weak hypotheses from the beginning of the training and trains all of them in parallel. Thus, the weak hypotheses are able to cooperate with each other during training. Also, we changed the function which update the weights of the samples to suppress the effects of the weights of outliers. We compared the performances of the new algorithm on several error-correcting output codes and weak hypothesis types. It was found that the proposed PCEL improves the accuracies of multi-class classification task in most datasets.