数据流分类是数据流挖掘领域一项重要研究任务,目标是从不断变化的海量数据中捕获变化的类结构.目前,几乎没有框架可以同时处理数据流中常见的多类非平衡、概念漂移、异常点和标记样本成本高昂问题.基于此,提出一种非平衡数据流在线主...数据流分类是数据流挖掘领域一项重要研究任务,目标是从不断变化的海量数据中捕获变化的类结构.目前,几乎没有框架可以同时处理数据流中常见的多类非平衡、概念漂移、异常点和标记样本成本高昂问题.基于此,提出一种非平衡数据流在线主动学习方法(Online active learning method for imbalanced data stream,OALM-IDS).AdaBoost是一种将多个弱分类器经过迭代生成强分类器的集成分类方法,AdaBoost.M2引入了弱分类器的置信度,此类方法常用于静态数据.定义了基于非平衡比率和自适应遗忘因子的训练样本重要性度量,从而使AdaBoost.M2方法适用于非平衡数据流,提升了非平衡数据流集成分类器的性能.提出了边际阈值矩阵的自适应调整方法,优化了标签请求策略.将概念漂移程度融入模型构建过程中,定义了基于概念漂移指数的自适应遗忘因子,实现了漂移后的模型重构.在6个人工数据流和4个真实数据流上的对比实验表明,提出的非平衡数据流在线主动学习方法的分类性能优于其他5种非平衡数据流学习方法.展开更多
流数据作为一种新型数据,在各个领域均有应用,其快速、大量及持续不断的特点使得单遍精准扫描成为在线学习算法的必备特质.在流数据不断产生过程中,往往会发生概念漂移,目前对于概念漂移节点检测的研究相对成熟,然而实际问题中学习环境...流数据作为一种新型数据,在各个领域均有应用,其快速、大量及持续不断的特点使得单遍精准扫描成为在线学习算法的必备特质.在流数据不断产生过程中,往往会发生概念漂移,目前对于概念漂移节点检测的研究相对成熟,然而实际问题中学习环境因素朝不同方向发展往往会导致流数据中概念漂移类别的多样性,这给流数据挖掘及在线学习带来了新的挑战.针对这个问题,提出一种基于时序窗口的概念漂移类别检测(concept drift class detection based on time window,CD-TW)方法.该方法借助栈和队列对流数据进行存取,借助窗口机制对流数据进行分块学习.首先创建2个分别加载历史数据和当前数据的基础节点时序窗口,通过比较二者所包含数据的分布变化情况来检测概念漂移节点.然后创建加载漂移节点后部分数据的跨度时序窗口,通过分析该窗口中数据分布的稳定性检测漂移跨度,进而判断概念漂移类别.实验结果表明该方法不仅能够精确定位概念漂移节点,同时在漂移类别判断方面也表现出良好性能.展开更多
The main challenges of data streams classification include infinite length, concept-drifting, arrival of novel classes and lack of labeled instances. Most existing techniques address only some of them and ignore other...The main challenges of data streams classification include infinite length, concept-drifting, arrival of novel classes and lack of labeled instances. Most existing techniques address only some of them and ignore others. So an ensemble classification model based on decision-feedback（ECM-BDF） is presented in this paper to address all these challenges. Firstly, a data stream is divided into sequential chunks and a classification model is trained from each labeled data chunk. To address the infinite length and concept-drifting problem, a fixed number of such models constitute an ensemble model E and subsequent labeled chunks are used to update E. To deal with the appearance of novel classes and limited labeled instances problem, the model incorporates a novel class detection mechanism to detect the arrival of a novel class without training E with labeled instances of that class. Meanwhile, unsupervised models are trained from unlabeled instances to provide useful constraints for E. An extended ensemble model Ex can be acquired with the constraints as feedback information, and then unlabeled instances can be classified more accurately by satisfying the maximum consensus of Ex. Experimental results demonstrate that the proposed ECM-BDF outperforms traditional techniques in classifying data streams with limited labeled data.展开更多
文摘数据流分类是数据流挖掘领域一项重要研究任务,目标是从不断变化的海量数据中捕获变化的类结构.目前,几乎没有框架可以同时处理数据流中常见的多类非平衡、概念漂移、异常点和标记样本成本高昂问题.基于此,提出一种非平衡数据流在线主动学习方法(Online active learning method for imbalanced data stream,OALM-IDS).AdaBoost是一种将多个弱分类器经过迭代生成强分类器的集成分类方法,AdaBoost.M2引入了弱分类器的置信度,此类方法常用于静态数据.定义了基于非平衡比率和自适应遗忘因子的训练样本重要性度量,从而使AdaBoost.M2方法适用于非平衡数据流,提升了非平衡数据流集成分类器的性能.提出了边际阈值矩阵的自适应调整方法,优化了标签请求策略.将概念漂移程度融入模型构建过程中,定义了基于概念漂移指数的自适应遗忘因子,实现了漂移后的模型重构.在6个人工数据流和4个真实数据流上的对比实验表明,提出的非平衡数据流在线主动学习方法的分类性能优于其他5种非平衡数据流学习方法.
文摘流数据作为一种新型数据,在各个领域均有应用,其快速、大量及持续不断的特点使得单遍精准扫描成为在线学习算法的必备特质.在流数据不断产生过程中,往往会发生概念漂移,目前对于概念漂移节点检测的研究相对成熟,然而实际问题中学习环境因素朝不同方向发展往往会导致流数据中概念漂移类别的多样性,这给流数据挖掘及在线学习带来了新的挑战.针对这个问题,提出一种基于时序窗口的概念漂移类别检测(concept drift class detection based on time window,CD-TW)方法.该方法借助栈和队列对流数据进行存取,借助窗口机制对流数据进行分块学习.首先创建2个分别加载历史数据和当前数据的基础节点时序窗口,通过比较二者所包含数据的分布变化情况来检测概念漂移节点.然后创建加载漂移节点后部分数据的跨度时序窗口,通过分析该窗口中数据分布的稳定性检测漂移跨度,进而判断概念漂移类别.实验结果表明该方法不仅能够精确定位概念漂移节点,同时在漂移类别判断方面也表现出良好性能.
基金supported by the National Natural Science Foundation of China(61202082)the Fundamental Research Funds for the Central Universities(BUPT2012RC0218,BUPT2012RC0219)
文摘The main challenges of data streams classification include infinite length, concept-drifting, arrival of novel classes and lack of labeled instances. Most existing techniques address only some of them and ignore others. So an ensemble classification model based on decision-feedback（ECM-BDF） is presented in this paper to address all these challenges. Firstly, a data stream is divided into sequential chunks and a classification model is trained from each labeled data chunk. To address the infinite length and concept-drifting problem, a fixed number of such models constitute an ensemble model E and subsequent labeled chunks are used to update E. To deal with the appearance of novel classes and limited labeled instances problem, the model incorporates a novel class detection mechanism to detect the arrival of a novel class without training E with labeled instances of that class. Meanwhile, unsupervised models are trained from unlabeled instances to provide useful constraints for E. An extended ensemble model Ex can be acquired with the constraints as feedback information, and then unlabeled instances can be classified more accurately by satisfying the maximum consensus of Ex. Experimental results demonstrate that the proposed ECM-BDF outperforms traditional techniques in classifying data streams with limited labeled data.
