Three approaches to deal with inconsistent decision tables - Comparison of decision tree complexity
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
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AbstractIn inconsistent decision tables, there are groups of rows with equal values of conditional attributes and different decisions (values of the decision attribute). We study three approaches to deal with such tables. Instead of a group of equal rows, we consider one row given by values of conditional attributes and we attach to this row: (i) the set of all decisions for rows from the group (many-valued decision approach); (ii) the most common decision for rows from the group (most common decision approach); and (iii) the unique code of the set of all decisions for rows from the group (generalized decision approach). We present experimental results and compare the depth, average depth and number of nodes of decision trees constructed by a greedy algorithm in the framework of each of the three approaches. © 2013 Springer-Verlag.
PublisherSpringer Science + Business Media
Conference/Event name14th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2013