Authors:
Mohammad Azad
and
Mikhail Moshkov
Affiliation:
King Abdullah University of Science and Technology, Saudi Arabia
Keyword(s):
Optimization, Decision Trees, Dynamic Programming, Greedy Heuristics, Many-valued Decisions.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Technologies
;
Mining Text and Semi-Structured Data
;
Operational Research
;
Optimization
;
Soft Computing
;
Symbolic Systems
Abstract:
A greedy algorithm has been presented in this paper to construct decision trees for three different approaches (many-valued decision, most common decision, and generalized decision) in order to handle the inconsistency of multiple decisions in a decision table. In this algorithm, a greedy heuristic ‘misclassification error’ is used which performs faster, and for some cost function, results are better than ‘number of boundary subtables’ heuristic in literature. Therefore, it can be used in the case of larger data sets and does not require huge amount of memory. Experimental results of depth, average depth and number of nodes of decision trees constructed by this algorithm are compared in the framework of each of the three approaches.