Complex feature alternating decision tree
by Ye Chow Kuang, Melanie Po-Leen Ooi
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 9, No. 3/4, 2010

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.

Online publication date: Thu, 04-Nov-2010

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