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	Random Forest-Based Ensemble Estimator for Concrete Compressive Strength Prediction via AdaBoost Method
	Lv, Yuanxin1,2; Shi, Xiaoyu2; Ran, Longyu2,3; Shang, Mingsheng2
	2020
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.
语种	英语
DOI	10.1007/978-3-030-32591-6_60
会议(录)名称	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
页码	557-565
收录类别	EI
会议地点	Kunming, China
会议日期	July 20, 2019 - July 22, 2019