15-12-2025
جامعة الملك عبدالعزيز
KING ABDULAZIZ UNIVERSITY
Faculty of Computing and Information Technology in Rabigh
Document Details
Document Type
:
Article In Journal
Document Title
:
Consistency of randomized and finite sized decision tree ensembles
تماسك فرق شجرة القرارات العشوائية والمحدودة الحجم
Subject
:
Computer Science
Document Language
:
English
Abstract
:
Regression via classification (RvC) is a method in which a regression problem is converted into a classification problem. A discretization process is used to covert continuous target value to classes. The discretized data can be used with classifiers as a classification problem. In this paper, we use a discretization method, Extreme Randomized Discretization, in which bin boundaries are created randomly to create ensembles. We present an ensemble method for RvC problems. We show theoretically for a set of problems that if the number of bins is three, the proposed ensembles for RvC perform better than RvC with the equal-width discretization method. We use these results to show that infinite-sized ensembles, consisting of finite-sized decision trees, created by a pure randomized method (split points are created randomly), are not consistent. We also theoretically show, using a set of regression problems, that the performance of these ensembles is dependent on the size of member decision trees
ISSN
:
1433-7541
Journal Name
:
Pattern Analysis and Applications
Volume
:
17
Issue Number
:
1
Publishing Year
:
1435 AH
2014 AD
Article Type
:
Article
Added Date
:
Monday, December 8, 2014
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
امير احمد
Ahmad, Amir
Investigator
Doctorate
amirahmad01@gmail.com
سامي محمد حلواني
Halawani, Sami M.
Researcher
Doctorate
Dr.Halawani@gmail.com
ابراهيم البديوي
Albidewi, Ibrahim
Researcher
Doctorate
ialbidewi@kau.edu.sa
Files
File Name
Type
Description
37605.pdf
pdf
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