Title: Complex feature alternating decision tree

Authors: Ye Chow Kuang, Melanie Po-Leen Ooi

Addresses: 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

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.

Keywords: decision trees; boosting; supervised learning; multiclass; alternating decision tree; ADTree; complex number features; complex domain features.

DOI: 10.1504/IJISTA.2010.036587

International Journal of Intelligent Systems Technologies and Applications, 2010 Vol.9 No.3/4, pp.335 - 353

Received: 19 Dec 2009
Accepted: 21 Feb 2010

Published online: 04 Nov 2010 *

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