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Top-Down Induction of Decision Trees Classifiers—A Survey

2005; Institute of Electrical and Electronics Engineers; Volume: 35; Issue: 4 Linguagem: Inglês

10.1109/tsmcc.2004.843247

1558-2442

Lior Rokach, Oded Maimon,

Imbalanced Data Classification Techniques

Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of growing a decision tree from available data. This paper presents an updated survey of current methods for constructing decision tree classifiers in a top-down manner. The paper suggests a unified algorithmic framework for presenting these algorithms and describes the various splitting criteria and pruning methodologies.