A hierarchical selective ensemble randomized neural network hybridized with heuristic feature selection for estimation of sea-ice thickness

Mozaffari, Ahmad; Scott, K. Andrea; Azad, Nasser L.; Chenouri, Shojaeddin

doi:10.1007/s10489-016-0815-x

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…Hierarchical ensemble is a frequently used method that considers both diversity and quality in building an ensemble model. Many algorithms are applied to build hierarchical ensembles (Kim, Lin, Choi, & Choi, 2016;Li, Sun, Li, & Yan, 2013;Mozaffari, Scott, & Azad, 2016;Su, Shan, Chen, & Gao, 2009). This study also uses hierarchical ensemble and puts forward a two-layer hierarchical selective ensemble model with NN (TLHSE-NN) to handle BFP for Chinese listed companies.…”

Section: Introductionmentioning

confidence: 99%

Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

Liu

2019

Journal of Forecasting

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More and more ensemble models are used to forecast business failure. It is generally known that the performance of an ensemble relies heavily on the diversity between each base classifier. To achieve diversity, this study uses kernel‐based fuzzy c‐means (KFCM) to organize firm samples and designs a hierarchical selective ensemble model for business failure prediction (BFP). First, three KFCM methods—Gaussian KFCM (GFCM), polynomial KFCM (PFCM), and Hyper‐tangent KFCM (HFCM)—are employed to partition the financial data set into three data sets. A neural network (NN) is then adopted as a basis classifier for BFP, and three sets, which are derived from three KFCM methods, are used to build three classifier pools. Next, classifiers are fused by the two‐layer hierarchical selective ensemble method. In the first layer, classifiers are ranked based on their prediction accuracy. The stepwise forward selection method is employed to selectively integrate classifiers according to their accuracy. In the second layer, three selective ensembles in the first layer are integrated again to acquire the final verdict. This study employs financial data from Chinese listed companies to conduct empirical research, and makes a comparative analysis with other ensemble models and all its component models. It is the conclusion that the two‐layer hierarchical selective ensemble is good at forecasting business failure.

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Section: Introductionmentioning

confidence: 99%

Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

Liu

2019

Journal of Forecasting

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Analysis and Prediction of Sea Ice Extent Using Statistical and Deep Learning Approach

Pinninti,

Dey,

Abdul Alim

et al. 2023

Lecture Notes in Networks and Systems

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Heuristic parallel selective ensemble algorithm based on clustering and improved simulated annealing

2018

J Supercomput

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A hierarchical selective ensemble randomized neural network hybridized with heuristic feature selection for estimation of sea-ice thickness

Cited by 5 publications

References 37 publications

Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

Analysis and Prediction of Sea Ice Extent Using Statistical and Deep Learning Approach

Heuristic parallel selective ensemble algorithm based on clustering and improved simulated annealing

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