Complex feature alternating decision tree

Authors:
Ye Chow Kuang;Melanie Po-Leen Ooi
Affiliations:
School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 46150, Bandar Sunway, Selangor, Malaysia.;School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 46150, Bandar Sunway, Selangor, Malaysia
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2010

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Abstract

Complex number features are ubiquitous in many engineering and scientific applications. Many traditional classification algorithms including alternating decision tree (ADTree) are very powerful but not capable of handling complex domain data. ADTree is classifier that is intrinsically support boosting, hence inherent all desirable statistical properties of boosting methodology. This work introduces base learners that enable application of ADTree algorithm to complex domain data. The presented results show that the proposed base learners enhance performance of ADTrees on complex domain features.