The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class cla...The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class classification in the case of insufficient samples,this paper proposes a multi-class classification method combining K-means and multi-task relationship learning(MTRL).The method first uses the split method of One vs.Rest to disassemble the multi-class classification task into binary classification tasks.K-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training costs.Finally,the sampled dataset is applied to the MTRL,and multiple binary classifiers are trained together.With the help of MTRL,this method can utilize the inter-task association to train the model,and achieve the purpose of improving the classification accuracy of each binary classifier.The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset,Wine dataset,Multiple Features dataset,Wireless Indoor Localization dataset and Avila dataset.展开更多
长尾分类在现实世界中是一项不可避免且充满挑战的任务。传统方法通常只专注于类间的不平衡分布,然而近期的研究开始重视类内的长尾分布,即同一类别内,具有头部属性的样本远多于尾部属性的样本。由于属性的隐含性和其组合的复杂性,类内...长尾分类在现实世界中是一项不可避免且充满挑战的任务。传统方法通常只专注于类间的不平衡分布,然而近期的研究开始重视类内的长尾分布,即同一类别内,具有头部属性的样本远多于尾部属性的样本。由于属性的隐含性和其组合的复杂性,类内不平衡问题更加难以处理。为此,文中提出一种基于引领森林并使用多中心损失的广义长尾分类框架(Cognisance),旨在通过不变性特征学习的范式建立长尾分类问题的多粒度联合求解模型。首先,该框架通过无监督学习构建粗粒度引领森林(Coarse-Grained Leading Forest,CLF),以更好地表征类内关于不同属性的样本分布,进而在不变风险最小化的过程中构建不同的环境。其次,设计了一种新的度量学习损失,即多中心损失(Multi-Center Loss,MCL),可在特征学习过程中逐步消除混淆属性。同时,Cognisance不依赖于特定模型结构,可作为独立组件与其他长尾分类方法集成。在ImageNet-GLT和MSCOCO-GLT数据集上的实验结果显示,所提框架取得了最佳性能,现有方法通过与本框架集成,在Top1-Accuracy指标上均获得2%~8%的提升。展开更多
基金supported by the National Natural Science Foundation of China(61703131 61703129+1 种基金 61701148 61703128)
文摘The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class classification in the case of insufficient samples,this paper proposes a multi-class classification method combining K-means and multi-task relationship learning(MTRL).The method first uses the split method of One vs.Rest to disassemble the multi-class classification task into binary classification tasks.K-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training costs.Finally,the sampled dataset is applied to the MTRL,and multiple binary classifiers are trained together.With the help of MTRL,this method can utilize the inter-task association to train the model,and achieve the purpose of improving the classification accuracy of each binary classifier.The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset,Wine dataset,Multiple Features dataset,Wireless Indoor Localization dataset and Avila dataset.
文摘长尾分类在现实世界中是一项不可避免且充满挑战的任务。传统方法通常只专注于类间的不平衡分布,然而近期的研究开始重视类内的长尾分布,即同一类别内,具有头部属性的样本远多于尾部属性的样本。由于属性的隐含性和其组合的复杂性,类内不平衡问题更加难以处理。为此,文中提出一种基于引领森林并使用多中心损失的广义长尾分类框架(Cognisance),旨在通过不变性特征学习的范式建立长尾分类问题的多粒度联合求解模型。首先,该框架通过无监督学习构建粗粒度引领森林(Coarse-Grained Leading Forest,CLF),以更好地表征类内关于不同属性的样本分布,进而在不变风险最小化的过程中构建不同的环境。其次,设计了一种新的度量学习损失,即多中心损失(Multi-Center Loss,MCL),可在特征学习过程中逐步消除混淆属性。同时,Cognisance不依赖于特定模型结构,可作为独立组件与其他长尾分类方法集成。在ImageNet-GLT和MSCOCO-GLT数据集上的实验结果显示,所提框架取得了最佳性能,现有方法通过与本框架集成,在Top1-Accuracy指标上均获得2%~8%的提升。
