Title: Signal prediction based on boosting and decision stump

Authors: Lei Shi; Qiguo Duan; Ping Dong; Lei Xi; Xinming Ma

Addresses: College of Information and Management Science, HeNan Agricultural University, Zhengzhou, HeNan, 450002, China ' Zhengzhou Commodity Exchange, Zhengzhou, 450008, China ' College of Information and Management Science, HeNan Agricultural University, Zhengzhou, HeNan, 450002, China ' College of Information and Management Science, HeNan Agricultural University, Zhengzhou, HeNan, 450002, China ' Collaborative Innovation Center of Henan Grain Crops, Zhengzhou, HeNan, 450002, China

Abstract: Signal prediction has attracted more and more attention from data mining and machine learning communities. Decision stump is a one-level decision tree, and it classifies instances by sorting them based on feature values. The boosting is a kind of powerful ensemble method and can improve the performance of prediction significantly. In this paper, boosting and decision stump algorithm are combined to analyse and predict the signal data. An experimental evaluation is carried out on the public signal dataset and the experimental results show that the boosting and decision stump-based algorithm clearly improves performance of signal prediction.

Keywords: decision stump; boosting; signal prediction.

DOI: 10.1504/IJCSE.2018.090450

International Journal of Computational Science and Engineering, 2018 Vol.16 No.2, pp.117 - 122

Received: 12 Jan 2016
Accepted: 10 May 2016
Published online: 19 Mar 2018 *

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