This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

We study problems of optimization of decision and inhibitory trees for decision tables with
many-valued decisions. As cost functions, we consider depth, average depth, number of …

In the presented study, the problem of interactive feature extraction, ie, supported by
interaction with users, is discussed, and several innovative approaches to automating …

Decision tree is a widely used technique to discover patterns from consistent data set. But if
the data set is inconsistent, where there are groups of examples (objects) with equal values …

Decision trees are one of the most commonly used tools in decision analysis, knowledge
representation, machine learning, etc., for its simplicity and interpretability. We consider an …

决策树技术在数据挖掘的分类领域中被广泛采用. 采用决策树从一致决策表(即条件属性值相同
的样本其决策值相同) 中挖掘有价值信息的相关研究较为成熟, 而对于非一致决策表(即条件属性 …

In the dissertation, the problem of interactive feature extraction, ie, supported by interaction
with users, is discussed, and several innovative approaches to automating feature creation …

This paper focuses on finding minimal complexities of decision trees representing rules
defined for features and functionalities of online learning community. Because …

In this chapter, we study bi-criteria optimization problem cost versus cost for decision and
inhibitory trees. We design an algorithm which constructs the set of Pareto optimal points for …