Random Forest-Based Ensemble Estimator for Concrete Compressive Strength Prediction via AdaBoost Method

doi:10.1007/978-3-030-32591-6_60

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 大数据挖掘及应用中心

	Random Forest-Based Ensemble Estimator for Concrete Compressive Strength Prediction via AdaBoost Method
	Lv, Yuanxin 1,3; Shi, Xiaoyu 1; Ran, Longyu 1,2; Shang, Mingsheng 1
	2020
会议日期	July 20, 2019 - July 22, 2019
会议地点	Kunming, China
DOI	10.1007/978-3-030-32591-6_60
页码	557-565
英文摘要	As one of the most important building materials, the quality of concrete directly affects the safety of buildings. Hence, it is an important and hot issue to predict the compressive strength of concrete with highly accuracy. Most of existing methods heavily depend on building a single model to predict the compressive strength of concrete. However, the proposed single models are not one-size-fits-all, different model have different limitations, making them a better or worse fit for different situation. To address this issue, this paper proposes a novel predict model for concrete compressive strength based on ensemble learning method. In detail, we build our ensemble framework via using the AdaBoost method, while the random forests methods as weak classifier are integrated to the AdaBoost framework. For dealing with the noisy and missing value problems, a set of statistical methods are employed. Furthermore, we utilize the Pearson correlation coefficient to analysis the relationship between different input materials, which can effectively drop out the irrelevant and redundant features. Experimental results on two industrial data sets show that proposed ensemble estimator can significantly improve the prediction accuracy in comparing with other five state-of-the-art methods. © 2020, Springer Nature Switzerland AG.
会议录	15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019
语种	英语
电子版国际标准刊号	21945365
ISSN号	21945357
内容类型	会议论文
源URL	[http://119.78.100.138/handle/2HOD01W0/9776]
专题	大数据挖掘及应用中心
作者单位	1.Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing; 400714, China; 2.University of Chinese Academy of Sciences, Beijing; 100049, China 3.College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing; 400065, China;
推荐引用方式 GB/T 7714	Lv, Yuanxin,Shi, Xiaoyu,Ran, Longyu,et al. Random Forest-Based Ensemble Estimator for Concrete Compressive Strength Prediction via AdaBoost Method[C]. 见:. Kunming, China. July 20, 2019 - July 22, 2019.