Greedy Algorithm for Construction of Decision Trees for Tables with Many-Valued Decisions.
Mohammad Azad, Igor Chikalov, Mikhail Moshkov, Beata Marta Zielosko · 2012
Abstract. In the paper, we study a greedy algorithm for construction of approximate 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. We use an uncertainty measure which is the number of boundary subtables. We present also experimental results for data sets from UCI Machine Learning Repository for proposed approach and approach based on generalized decision.