Relationships among various parameters for decision tree optimization

Type
Article

Authors
Hussain, Shahid

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program

Online Publication Date
2013-11-15

Print Publication Date
2014

Date
2013-11-15

Abstract
In this chapter, we study, in detail, the relationships between various pairs of cost functions and between uncertainty measure and cost functions, for decision tree optimization. We provide new tools (algorithms) to compute relationship functions, as well as provide experimental results on decision tables acquired from UCI ML Repository. The algorithms presented in this paper have already been implemented and are now a part of Dagger, which is a software system for construction/optimization of decision trees and decision rules. The main results presented in this chapter deal with two types of algorithms for computing relationships; first, we discuss the case where we construct approximate decision trees and are interested in relationships between certain cost function, such as depth or number of nodes of a decision trees, and an uncertainty measure, such as misclassification error (accuracy) of decision tree. Secondly, relationships between two different cost functions are discussed, for example, the number of misclassification of a decision tree versus number of nodes in a decision trees. The results of experiments, presented in the chapter, provide further insight. © 2014 Springer International Publishing Switzerland.

Citation
Hussain, S. (2013). Relationships Among Various Parameters for Decision Tree Optimization. Innovations in Intelligent Machines-4, 393–410. doi:10.1007/978-3-319-01866-9_13

Publisher
Springer Nature

Journal
Studies in Computational Intelligence

DOI
10.1007/978-3-319-01866-9_13

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