Upper Bounds and Algorithms for Construction of Deterministic Decision Trees for Decision Tables. Second Approach
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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 \(\varLambda 3\).
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