Abstract
Random Forest is one of the most popular decision forest building algorithms that use decision trees as the base classifiers. The splitting attributes for decision trees of Random Forest are generally determined from a predefined number of randomly selected attribute subset of the original attribute set. In this paper, we propose a new technique that randomly determines the size of the attribute subset between a dynamically determined range based on the relative size of current data segment to the bootstrap samples at each node splitting event. We present elaborate experimental results involving five widely used data sets from the UCI Machine Learning Repository. The experimental results indicate the effectiveness of the proposed technique in the context of Random Forest.
Original language | English |
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Title of host publication | ADMA 2014 |
Editors | X. Luo, J.X. Yu, Z. Li |
Place of Publication | Cham, USA |
Pages | 370-379 |
Number of pages | 10 |
Volume | 8933 |
ISBN (Electronic) | 9783319147178 |
DOIs | |
Publication status | Published - 2014 |
Event | International Conference on Advanced Data Mining and Applications - Guangxi Normal University, Guilin Duration: 19 Dec 2014 → 21 Dec 2014 |
Conference
Conference | International Conference on Advanced Data Mining and Applications |
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Period | 19/12/14 → 21/12/14 |