In several applications of automatic diagnosis and active learning a central problem is the evaluation of a discrete function by adaptively querying the values of its variables until the values read uniquely determine the value of the function. In general reading the value of a variable is done at the expense of some cost (computational or possibly a fee to pay the corresponding experiment). The goal is to design a strategy for evaluating the function incurring little cost (in the worst case or in expectation according to a prior distribution on the possible variables' assignments). Our algorithm builds a strategy (decision tree) which attains a logarithmic approximation simultaneously for the expected and worst cost spent. This is best possible since, under standard complexity assumption, no algorithm can guarantee o(logn) approximation.

Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost

Cicalese, Ferdinando;
2014-01-01

Abstract

In several applications of automatic diagnosis and active learning a central problem is the evaluation of a discrete function by adaptively querying the values of its variables until the values read uniquely determine the value of the function. In general reading the value of a variable is done at the expense of some cost (computational or possibly a fee to pay the corresponding experiment). The goal is to design a strategy for evaluating the function incurring little cost (in the worst case or in expectation according to a prior distribution on the possible variables' assignments). Our algorithm builds a strategy (decision tree) which attains a logarithmic approximation simultaneously for the expected and worst cost spent. This is best possible since, under standard complexity assumption, no algorithm can guarantee o(logn) approximation.
2014
978-163439397-3
Approximation algorithms; Artificial intelligence; Costs; Data mining; Decision trees; Function evaluation; Learning systems Active Learning; Automatic diagnosis; Central problems; Complexity assumptions; Discrete functions; Logarithmic approximation; Prior distribution; Testing costs
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/881220
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