A new method for constructing granular neural networks based on rule extraction and extreme learning machine

Xiaoli, Xu; Wang, Guanying; Ding, Shifei; Jiang, Xiangying; Zhen, Zhao

doi:10.1016/j.patrec.2015.05.006

Cited by 10 publications

(5 citation statements)

References 30 publications

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“…The inferred posterior class probabilities of bankruptcy were used to analyze the sensitivity of the classifier output and assist in the credit assignment decision-making process. A hybrid model of neural network and adaptive boosting method was studied by Xu et al (2015). Sexton and Mcmurtrey (2006) and Setiono et al (2009) studied a genetic algorithm-based neural network algorithm for credit card screening and found that the neural network rule extraction is very effective in discovering knowledge and is particularly appropriate in applications that require comprehensibility and accuracy.…”

Section: Financial Crisis and Risk Modelingmentioning

confidence: 99%

Decision analytics and machine learning in economic and financial systems

Qiao

Beling

2016

Environ Syst Decis

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Decision analytics may be viewed as the combined use of predictive modeling techniques (e.g., forecasting and machine learning) and prescriptive decision frameworks (e.g., optimization and simulation) to create value in systems. Recent years have seen an explosion in decision analytics applications, driven by advances in machine learning methods and computational optimization and by massive increases in the data to which these techniques may be applied. Decision analytics has long been used in the domains of economic and financial systems, with credit scoring being an example of an early success, and the clear trend is to the development of ever more sophisticated methods and applications.This issue of Environment System & Decisions is devoted to decision analytics in financial and economic systems and environments. The papers in the issue each bear an influence from theoretical developments and financial and economic applications of statistical methods, machine learning, and decision and risk modeling techniques. Before introducing the papers, we review some of the influential literature in this regard.

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Section: Financial Crisis and Risk Modelingmentioning

confidence: 99%

Decision analytics and machine learning in economic and financial systems

Qiao

Beling

2016

Environ Syst Decis

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“…Granular Neural Networks (GNNs) were introduced in [1] as the amalgamation between Neural Networks and Granular Computing. Roughly speaking, this synergy aims at reconciling the black-box behavior and the lack of transparency of neural networks with the aid of information granules, such as classes, clusters, subsets, etc [2]. Through similarity among objects and granulation [3], GNNs bypass the use of large datasets and precise information, while building interpretable and lighter models.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models

Concepción

Nápoles

Grau

et al. 2022

IEEE Trans. Cybern.

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“…Ahn et al proposed a hybrid intelligent system combining rough set with artificial neural network to predict the failure of firms [31] and a new reduction algorithm called 2D reduction was designed; in 2D reduction method, rough set is utilized to eliminate irrelevant or redundant attributes and then scans samples to delete the samples of inconsistent decisions; at last, association rules can be extracted from the data; for Ahn's hybrid classification model, if a new instance matches some association rules, the instance will be classified by the association rules; otherwise, the algorithm will use the data reduced by 2D reduction method to train a classifier to classify this instance. Xu et al introduced a rough rule granular extreme learning machine called RRGELM [32]; RRGELM uses rough set to extract association rules and the number of neurons in hidden layer is decided by the number of the association rules; the weights of input layer is not randomly generated, they are determined by the conditions whether instances are covered by the association rules or not. The above works have promoted the developments of rough set and neural network.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional algorithms often separate rough sets and neural networks and do not fuse ELM and rough sets very well. Different from existing algorithms which only use rough set to reduce attributes or determine the number of neurons in hidden layer [31,32], RELM uses the results of the data divided by rough set to train different kinds of neurons; it preferably combines rough set with extreme learning machine.…”

Section: Introductionmentioning

confidence: 99%

Rough extreme learning machine: A new classification method based on uncertainty measure

Feng

Wang

et al. 2019

Neurocomputing

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Extreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated; the weights of the output layer can be analytically determined. ELM has been achieved good results for a large number of classification tasks. In this paper, a new extreme learning machine called rough extreme learning machine (RELM) was proposed. RELM uses rough set to divide data into upper approximation set and lower approximation set, and the two approximation sets are utilized to train upper approximation neurons and lower approximation neurons. In addition, an attribute reduction is executed in this algorithm to remove redundant attributes. The experimental results showed, comparing with the comparison algorithms, RELM can get a better accuracy and repeatability in most cases; RELM can not only maintain the advantages of fast speed, but also effectively cope with the classification task for high-dimensional data.

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A new method for constructing granular neural networks based on rule extraction and extreme learning machine

Cited by 10 publications

References 30 publications

Decision analytics and machine learning in economic and financial systems

Decision analytics and machine learning in economic and financial systems

Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models

Rough extreme learning machine: A new classification method based on uncertainty measure

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