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.
|Number of pages||12|
|Journal||IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews|
|State||Published - 1 Nov 2005|
- Decision trees
- Pruning methods
- Splitting criteria