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About Discretization Techniques A Recent Survey
This document summarizes recent discretization techniques used in machine learning algorithms. Discretization is needed to handle problems with continuous attributes by treating intervals of continuous values as nominal categories. The performance of decision trees, Bayesian networks, and rule learners depends on the right discretization method. The document reviews categories of discretization techniques, including supervised vs. unsupervised, direct vs. incremental, global vs. local, static vs. dynamic, and top-down vs. bottom-up approaches. It also describes the typical discretization process and evaluates cut-points and intervals.
Detailed Information
| Author: | ['tia'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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