A design framework for hierarchical ensemble of multiple feature extractors and multiple classifiers

Kim, Kyounghoon; Lin, Helin; Choi, Jin Young; Choi, Ki‐Young

doi:10.1016/j.patcog.2015.11.006

Cited by 22 publications

(11 citation statements)

References 45 publications

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“…For the various classifiers, it was proved that the integration and cross-validation can obtain better results [33,34]. Therefore, scholars put forward integrated learning methods to combine single classifiers.…”

Section: Methods Of Expert Selection and Weight Definitionmentioning

confidence: 99%

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

et al. 2020

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Algal bloom is a typical pollution of urban lakes, which threatens drinking safety and breaks the urban landscape. It is pivotal to select a reasonable governance approach for sustainable management. A decision-making support method was studied in this paper. First, a general framework was designed to organize the rational decision-making processes. Second, quantitative calculation methods were proposed, including expert selection and opinion integration. The methods can determine the vital decision elements objectively and automatically. Third, the method was applied in Yuyuantan Lake in Beijing, China. The monitoring information and decision-making process are presented and the rank of governance alternatives is given. The comparison and discussion show that the group decision-making method is feasible and effective. It can assist the sustainable management of algal bloom.

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Section: Methods Of Expert Selection and Weight Definitionmentioning

confidence: 99%

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

et al. 2020

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“…In general, these algorithms have a set of baseclassifiers with a fixed capacity and use a passive learning mechanism, such as Streets Streaming Ensemble Algorithm (SEA) [20] and Chens Recursive Ensemble Approach (REA) [21]. These algorithms use the newest trained base-classifier to replace the oldest one in the set to construct a new ensemble [22,23]. Some of the algorithms use the newest trained base-classifier to replace the weakest one in the set to construct a new ensemble, such as the Kolters Dynamic Weighted Majority online algorithm (DWM) [24].…”

Section: Relevant Algorithmsmentioning

confidence: 99%

A fast learn++.NSE classification algorithm based on weighted moving average

Shen¹,

Zhu²,

Du³

et al. 2018

Filomat

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Current researches of incremental classification learning algorithms mainly focus on learning from data in a stationary environment. The incremental learning in a non-stationary environment (NSE), where the underlying data probability distribution changes over time, however, has received much less attentions despite the abundant real applications have generated the long-term and cumulative big data in NSE. Thus, the incremental learning in NSE has gradually received extensive attentions. Nevertheless, the popular incremental classification learning algorithms currently for NSE such as SEA and DWM generally place strict restrictions on the changes. These algorithms can only deal with gradual drift and noncyclical and no new category situations. Therefore, it is highly necessary to develop a novel efficient incremental classification learning algorithm for the gradually cumulative big data in complex NSE. The recently proposed Learn++.NSE algorithm is an important research achievement in this field. However, the vote weight of each base-classifier of the Learn++.NSE depends on its whole error rates in the environments experienced. Therefore, the classification learning efficiency of the Learn++.NSE should be further improved. A novel fast Learn++.NSE algorithm based on weighted moving average (WMA-Learn++.NSE) is presented in this paper, which computes the weighted average of error rates using the sliding window technology to optimize the weight calculation. By only using the recent classification error rates of each base-classifier inside the sliding window to calculate the vote weight, the WMA-Learn++.NSE accelerates the compute of vote weight and improves the efficiency of classification learning. The verification experiments and performance analyses on both synthetic and real data set are presented in this paper. The experimental results show that the WMA-Learn++.NSE can achieve a higher execution efficiency compared to the Learn++.NSE in getting the equivalent classification correct rate.

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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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A design framework for hierarchical ensemble of multiple feature extractors and multiple classifiers

Cited by 22 publications

References 45 publications

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

A fast learn++.NSE classification algorithm based on weighted moving average

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

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