Minimization of Decision Tree Depth for Multi-label Decision Tables

被引：0
|
作者：
Azad, Mohammad [1 ]
Moshkov, Mikhail [1 ]
机构：
[1] King Abdullah Univ Sci & Technol, Comp Elect & Math Sci & Engn Div, Thuwal 239556900, Saudi Arabia
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING (GRC) | 2014年
关键词：
depth; decision tree; greedy algorithm; dynamic programming; multi-label decision table;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：7 / 12
页数：6
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