Top-Down Induction of Decision Trees Classifiers - a Survey  

Oleh:

Rokach, Lior ; Maimon, Oded

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Systems, Man, and Cybernetics: Part C Applications and Reviews vol. 35 no. 4 (Nov. 2005), page 476-487.
Topik: Classification; Decision Trees; Pruning Methods; Splitting Criteria

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II69.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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