Document Details
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Document Type |
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Article In Journal |
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Document Title |
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Consistency of randomized and finite sized decision tree ensembles تماسك فرق شجرة القرارات العشوائية والمحدودة الحجم |
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Subject |
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Computer Science |
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Document Language |
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English |
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Abstract |
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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 |
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ISSN |
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1433-7541 |
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Journal Name |
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Pattern Analysis and Applications |
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Volume |
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17 |
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Issue Number |
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1 |
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Publishing Year |
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1435 AH
2014 AD |
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Article Type |
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Article |
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Added Date |
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Monday, December 8, 2014 |
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Researchers
| 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 |
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