Abstract
In this chapter, upper bounds on the minimum complexity and algorithms for construction of deterministic decision trees for decision tables are considered. These bounds and algorithms are based on the use of so-called additive-bounded uncertainty measures for decision tables. The bounds are true for any complexity function having the property \(\varLambda 1\). When developing algorithms, we assume that the complexity functions have properties \(\varLambda 1\), \(\varLambda 2\), and \(\varLambda 3\).
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Published version (
Free)
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
