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On relationships among various cost functions and with an uncertainty measure for decision trees

Speaker(s)
Shahid Hussain
Affiliation
KAUST
Date
June 17, 2011, 2:15 p.m.
Room
room 5820
Seminar
Research Seminar of the Logic Group: Approximate reasoning in data mining

This talk is devoted to the design of new tools for studying exact and approximate decision trees (α-decision trees). We present algorithms to study the relationships between number of misclassification and number of nodes with the depth of an exact decision tree. We also present an algorithm that describes relationships between these and other cost functions (such as average depth, number of terminal nodes, etc) with a measure of uncertainty for approximate decision tree α -decision trees). Furthermore, we provide results of experiments conducted with these algorithms on decision tables (datasets) acquired from UCI ML Repository.