Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
On Dynamic Selection of Subspace for Random Forest
verfasst von : Md Nasim Adnan
Erschienen in: Advanced Data Mining and Applications
Verlag: Springer International Publishing
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.