Modeling collinear data using double-layer GA-based selective ensemble kernel partial least squares algorithm

详细信息    查看全文

• 作者：Jian Tang^a ; ^b ; ^cAuthor Vitae ; Jian Zhang^a ; ^{jianzhang_neu@163.com}Author Vitae ; Zhiwei Wu^bAuthor Vitae ; Zhuo Liu^bAuthor Vitae ; Tianyou Chai^bAuthor Vitae ; Wen Yu^dAuthor Vitae
• 关键词：Collinear and nonlinear data modeling ; Latent variable (LV) ; Selective ensemble learning ; Double-layer genetic algorithm (DLGA) optimization ; Kernel partial least squares (KPLS)
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：5 January 2017
• 年：2017
• 卷：219
• 期：Complete
• 页码：248-262
• 全文大小：1217 K
• 卷排序：219

文摘

Collinear and nonlinear characteristics of modeling data have to be addressed for constructing effective soft measuring models. Latent variables (LVs)-based modeling approaches, such as kernel partial least squares (KPLS), can overcome these disadvantages in certain degree. Selective ensemble (SEN) modeling can improve generalization performance of learning models further. Nevertheless, how to select SEN model's learning parameters is an important open issue. In this paper, a novel SENKPLS modeling method based on double-layer genetic algorithm (DLGA) optimization is proposed. At first, one mechanism, titled outside layer adaptive GA (AGA) optimization encoding and decoding principle, is employed to produce initial learning parameter values for KPLS-based candidate sub-models. Then, ensemble sub-models are selected and combined based on inside layer GA optimization toolbox (GAOT) and adaptive weighting fusion (AWF) algorithm. Thus, SEN models of all AGA populations are obtained. Finally, outside layer AGA optimization operations, i.e., selection, crossover and mutation processes, are repeated until the pre-set stopping criterion is satisfied. Simulation results validate the effectiveness of the proposed method as far as the synthetic data, low dimensional and high dimensional benchmark data.