"基于被动-主动策略的特征演化流学习算法及其集成方法"

基于被动-主动的特征演化流学习是一种有效的在线学习算法，适用于处理特征不断演化的数据流。在这种情况下，数据特征会随着时间的推移而变化，新的特征会出现，旧特征会消失。通过被动-主动更新策略，该算法可以从当前特征空间和已消失特征空间中学习两个模型，并通过组合预测和当前最优预测来提高整体性能。实验证明，该算法在合成数据集和真实数据集上均表现出很好的效果，对于在线学习、监督学习和演化特征在线学习具有广泛的应用前景。Passive-Aggressive Learning with Feature Evolvable Streams (PAFE) algorithm is a fine-tuned online learning algorithm tailored for dealing with data streams featuring evolving characteristics. In such scenarios, data attributes fluctuate over time as new sensors replace old ones, hence introducing new features and obsoleting old ones. Through the passive-aggressive update strategy, the algorithm is capable of learning two models from the current feature space and the restored vanished feature space. By leveraging ensemble techniques like combined prediction and current optimal prediction, the overall performance of the algorithm is significantly enhanced. Empirical evidence confirms the effectiveness of the algorithm on both synthetic and real-world datasets, implying its wide applicability in online learning, supervised learning, and evolving feature online learning.