AdaBoost-based conformal prediction with high efficiency. (15th April 2019)

• Record Type: Journal Article
• Authors: Zhang, Yingjie; Xu, Jianxing; Cheng, Hengda
• Is Part Of: International journal of high performance computing and networking. Volume 13:Number 4(2019)
• Page Start: 355
• Page End: 365
• Publication Date: 2019-04-15
• Subjects: machine learning -- conformal prediction -- AdaBoost -- efficiency -- ensemble -- support vector machine -- decision tree -- weak classifiers -- p-value -- prediction label; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

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.