一种挖掘概念漂移数据流的选择性集成算法

提出一种挖掘概念漂移数据流的选择性集成学习算法。该算法根据各基分类器在验证集上的输出结果向量方向与参考向量方向之间的偏离程度,选择参与集成的基分类器。分别在具有突发性和渐进性概念漂移的人造数据集SEA和Hyperplane上进行实验分析。实验结果表明,这种基分类器选择方法大幅度提高了集成算法在处理概念漂移数据流时的分类准确性。使用error-ambiguity分解对算法构建的naive Bayes集成在解决分类问题时的性能进行了分析。实验结果表明,算法成功的主要原因是它能显著降低平均泛化误差。

Selected Ensemble of Classifiers for Handling Concept-drifting Data Streams

In data streams concept is often not stable but change with time.We proposed a selective integration algorithm OSEN (Orientation based Selected ENsemble) for handling concept drift data streams.This algorithm selects a near optimal subset of base classifiers based on the output of each base classifier on validation dataset.Our experiments with synthetic data sets simulating abrupt (SEA) and gradual (Hyperplane) concept drifts demonstrate that selective integration of classifiers built over small time interv...