Minimization of decision tree depth for multi-label decision tables

Author

Azad, Mohammad ; Moshkov, Mikhail

Author_Institution

Electr. & Math. Sci. & Eng. Div., King Abdullah Univ. of Sci. & Technol., Thuwal, Saudi Arabia

fYear

2014

fDate

22-24 Oct. 2014

Firstpage

7

Lastpage

12

Abstract

In this paper, we consider multi-label decision tables that have a set of decisions attached to each row. Our goal is to find one decision from the set of decisions for each row by using decision tree as our tool. Considering our target to minimize the depth of the decision tree, we devised various kinds of greedy algorithms as well as dynamic programming algorithm. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of depth of decision trees.

Keywords

data handling; decision tables; decision trees; dynamic programming; greedy algorithms; decision tree depth minimization; dynamic programming algorithm; greedy algorithms; multilabel decision tables; Decision trees; Entropy; Greedy algorithms; Heuristic algorithms; Impurities; Measurement uncertainty; Uncertainty; decision tree; depth; dynamic programming; greedy algorithm; multi-label decision table;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing (GrC), 2014 IEEE International Conference on

Conference_Location

Noboribetsu

Type

conf

DOI

10.1109/GRC.2014.6982798

Filename

6982798