Title: AdaBoost-based conformal prediction with high efficiency

Authors: Yingjie Zhang; Jianxing Xu; Hengda Cheng

Addresses: College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, 300300, China ' College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, 300300, China ' Department of Computer Science, Utah State University, Logan, UT, 84322, USA

Abstract: Conformal prediction presents a novel idea whose error rate is provably controlled by given significant levels. So the remaining goal of conformal prediction is its efficiency. High efficiency means that the predictions are as certain as possible. As we know, ensemble methods are able to obtain a better predictive performance than that obtained from any of the constituent models. Ensemble method such as random forest has been used as underlying method to build conformal predictor. But we do not know the differences of conformal predictors with and without ensemble methods, and how the corresponding performances are improved. In this paper, an ensemble method AdaBoost is used to build conformal predictor, and we introduce another evaluation metric-correct efficiency, which measures the efficiency of correct classification correctly. The good performance of AdaBoost-based conformal predictor (CP-AB) has been validated on seven datasets. The experimental results show that the proposed method has a much higher efficiency.

Keywords: machine learning; conformal prediction; AdaBoost; efficiency; ensemble; support vector machine; decision tree; weak classifiers; p-value; prediction label.

DOI: 10.1504/IJHPCN.2019.099260

International Journal of High Performance Computing and Networking, 2019 Vol.13 No.4, pp.355 - 365

Received: 16 Jun 2016
Accepted: 12 Oct 2016
Published online: 24 Apr 2019 *

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