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A Greedy Algorithm for Construction of Decision Trees for Tables with Many-Valued Decisions : A Comparative Study

Azad M., Chikalov I., Moshkov M., Zielosko B.

Fundamenta Informaticae

2013

Vol. 128, nr 1-2

1--15

In the paper, we study a greedy algorithm for construction of decision trees. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. Experimental results for data sets from UCI Machine Learning Repository and randomly generated tables are presented. We make a comparative study of the depth and average depth of the constructed decision trees for proposed approach and approach based on generalized decision. The obtained results show that the proposed approach can be useful from the point of view of knowledge representation and algorithm construction.

Greedy Algorithms for Construction of Approximate Tests for Decision Tables with Many-Valued Decisions

Azad M., Chikalov I., Moshkov M., Zielosko B.

Fundamenta Informaticae

2012

Vol. 120, nr 3/4

231-242

The paper is devoted to the study of a greedy algorithm for construction of approximate tests (super-reducts). This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. We consider bounds on the precision of this algorithm relative to the cardinality of tests.