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 TypeDr GradeEmail
امير احمدAhmad, Amir InvestigatorDoctorateamirahmad01@gmail.com
سامي محمد حلوانيHalawani, Sami M.ResearcherDoctorateDr.Halawani@gmail.com
ابراهيم البديويAlbidewi, Ibrahim ResearcherDoctorateialbidewi@kau.edu.sa

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