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An incremental decision tree algorithm based on rough sets and its application in intrusion detection

机译：基于粗糙集的增量决策树算法及其在入侵检测中的应用

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摘要

As we know, learning in real world is interactive, incremental and dynamical in multiple dimensions, where new data could be appeared at anytime from anywhere and of any type. Therefore, incremental learning is of more and more importance in real world data mining scenarios. Decision trees, due to their characteristics, have been widely used for incremental learning. In this paper, we propose a novel incremental decision tree algorithm based on rough set theory. To improve the computation efficiency of our algorithm, when a new instance arrives, according to the given decision tree adaptation strategies, the algorithm will only modify some existing leaf node in the currently active decision tree or add a new leaf node to the tree, which can avoid the high time complexity of the traditional incremental methods for rebuilding decision trees too many times. Moreover, the rough set based attribute reduction method is used to filter out the redundant attributes from the original set of attributes. And we adopt the two basic notions of rough sets: significance of attributes and dependency of attributes, as the heuristic information for the selection of splitting attributes. Finally, we apply the proposed algorithm to intrusion detection. The experimental results demonstrate that our algorithm can provide competitive solutions to incremental learning.
机译：众所周知，现实世界中的学习是多维的交互，增量和动态的，新数据可以随时随地以任何类型出现。因此，增量学习在现实世界的数据挖掘场景中越来越重要。决策树由于其特性而被广泛用于增量学习。在本文中，我们提出了一种基于粗糙集理论的新型增量决策树算法。为了提高算法的计算效率，当新实例到达时，根据给定的决策树自适应策略，该算法将仅修改当前活动决策树中的某些现有叶节点或向该树添加新的叶节点，从而可以避免太多传统的增量方法来重建决策树的时间复杂性。此外，基于粗糙集的属性约简方法用于从原始属性集中滤除冗余属性。并且，我们采用粗糙集的两个基本概念：属性的重要性和属性的依赖性，作为用于选择分裂属性的启发式信息。最后，我们将提出的算法应用于入侵检测。实验结果表明，我们的算法可以为增量学习提供有竞争力的解决方案。

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