NDLI: Selective Hierarchical Ensemble Modeling Approach and Its Application in Leaching Process

Content Provider	IEEE Xplore Digital Library
Author	Guanghao Hu Fei Yang
Copyright Year	2015
Description	Author affiliation: Sch. of Inf. Eng., Shenyang Univ., Shenyang, China (Guanghao Hu) \|\| Sch. of Bus. Adm., Shenyang Univ., Shenyang, China (Fei Yang)
Abstract	To improve the precision and generalization of ensemble model and leaching model, a novel selective hierarchical ensemble modeling approach is proposed for leaching rate prediction in this paper. Unlike previous selective ensemble model, the new selective ensemble model is a hierarchical model. The model considers not only the combination of sub-models, but also the generation of sub-models. First of all, a new multi-model ensemble hybrid model (MEHM) based on bagging algorithm is proposed. In this model, the sub-models are composed of data model and mechanism model. The data model generates training subsets by using the proposed based vector bootstrap sampling algorithm. Afterwards, a new selective multi-model ensemble hybrid model (NSMEHM) based on binary particle swarm optimization (PSO) algorithm is presented. In this model, the binary PSO optimization algorithm is used to find out a group of the MEHMs, which minimizes the error and maximizes the diversity. Experiment results indicate that the proposed NSMEHM has better prediction performance than the other models.
Starting Page	554
Ending Page	561
File Size	417534
Page Count	8
File Format	PDF
e-ISBN	9781467393232
DOI	10.1109/ISKE.2015.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-24
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines selective ensemble leaching process Predictive models prediction Prediction algorithms Data models hierarchical model Sulfur Leaching Bagging
Content Type	Text
Resource Type	Article

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