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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Mining Recurring Concepts in a Dynamic Feature Space
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Mining Recurring Concepts in a Dynamic Feature Space

机译：在动态特征空间中挖掘重复概念

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摘要

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.
机译：大多数数据流分类技术都假定基础特征空间是静态的。但是，在实际应用中，功能集及其与目标概念的相关性可能会随时间变化。另外，当基本概念重新出现时，重新使用先前学习的模型可以在准确性和处理时间方面增强学习过程，但以可管理的内存消耗为代价。在本文中，我们提出在动态特征空间（MReC-DFS）中挖掘重复概念，MReC-DFS是一种数据流分类系统，旨在解决在动态特征空间中学习重复概念的挑战，同时降低与存储过去模型相关的内存成本。 MReC-DFS能够利用学习过程和上下文信息的性能来检测和适应概念的变化。为了处理重复出现的概念，将存储的模型合并到一个动态加权的集合中。执行增量特征选择以减少组合特征空间。这种贡献使MReC-DFS仅存储与所学概念最相关的功能，从而提高了该技术的存储效率。此外，提出了一种增量式特征选择方法，该方法动态确定相关特征和不相关特征之间的阈值。提出了实验结果，证明了MReC-DFS与各种实际数据集上的最新技术相比具有较高的准确性。结果还显示了MReC-DFS的卓越存储效率。

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