Abstract
The Random Forest algorithm generates quite diverse decision trees as the base classifiers for high dimensional data sets. However, for low dimensional data sets the diversity among the trees falls sharply. In Random Forest, the size of the bootstrap samples generally remains the same every time to generate a decision tree as the base classifier. In this paper we propose to vary the size of the bootstrap samples randomly within a predefined range in order to increase diversity among the trees. We conduct an elaborate experimentation on several low dimensional data sets from UCI Machine Learning Repository. The experimental results show the effectiveness of our proposed technique.
| Original language | English |
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| Title of host publication | ESANN 2015 proceedings |
| Subtitle of host publication | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
| Place of Publication | Belgium |
| Publisher | i6doc.com publication |
| Pages | 391-396 |
| Number of pages | 6 |
| ISBN (Print) | 9782875870148 |
| Publication status | Published - 2015 |
| Event | The 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Novotel Hotel, Bruges, Belgium Duration: 22 Apr 2015 → 24 Apr 2015 https://www.elen.ucl.ac.be/esann/ |
Conference
| Conference | The 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
|---|---|
| Country/Territory | Belgium |
| City | Bruges |
| Period | 22/04/15 → 24/04/15 |
| Other | This event builds upon a very successful series of conference organized each year since 1993. ESANN has become a major scientific events in the machine learning, computational intelligence and artificial neural networks fields over the years. The two main tracks are "Theory and methods", and "Information processing and applications". See call for papers for details. In addition, a number of special sessions will be organized, on selected hot topics in the machine learning, computational intelligence and artificial neural networks fields. See special sessions for details. |
| Internet address |