Adaptive building of decision trees by reinforcement learning
Adaptive building of decision trees by reinforcement learning
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通过强化学习自适应构建决策树
DOI：
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发表时间：
2007
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通讯作者：
M. Preda
中科院分区：
文献类型：
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作者：
M. Preda
决策树学习代表了一个众所周知的家庭的归纳学习算法，能够提取，从提出的训练集，分类规则的先决条件可以表示为析取的约束条件的合取。决策树的名称是由于这样一个事实，即前提条件可以表示为树，其中每个节点是一个约束，从根节点到叶节点的每条路径表示由约束的合取组成的析取，路径中的每个节点都有一个约束。由于它们的效率，这些方法被广泛应用于金融，工程和医疗等领域。提出了一种基于强化学习的决策树构造方法。新的构造方法变得越来越有效，因为它构造越来越多的决策树，因为它可以学习什么约束应该首先测试，以便准确和有效地分类训练集中的一个子集的例子。这一特性使得新方法适用于训练集频繁变化的问题，并且分类规则可以支持随时间的微小变化。当不同的约束具有不同的测试成本时，该方法也是有效的。本文最后的性能结果和所提出的算法的特点的总结。
Decision tree learning represents a well known family of inductive learning algorithms that are able to extract, from the presented training sets, classification rules whose preconditions can be represented as disjunctions of conjunctions of constraints. The name of decision trees is due to the fact that the preconditions can be represented as a tree where each node is a constraint and each path from the root to a leaf node represents a disjunction composed from a conjunction of constraints, one constraint for each node from the path. Due to their efficiency, these methods are widely used in a diversity of domains like financial, engineering and medical. The paper proposes a new method to construct decision trees based on reinforcement learning. The new construction method becomes increasingly efficient as it constructs more and more decision trees because it can learn what constraint should be tested first in order to accurately and efficiently classify a subset of examples from the training set. This feature makes the new method suitable for problems were the training set is changed frequently and also the classification rules can support slightly changes over time. The method is also effective when different constraints have different testing costs. The paper concludes with performance results and with a summary of the features of the proposed algorithm.