Rule Extraction from Neural Network Using Input Data Ranges Recursively

Chakraborty, Manomita; Biswas, Saroj Kumar; Purkayastha, Biswajit

doi:10.1007/s00354-018-0048-0

Cited by 20 publications

(9 citation statements)

References 19 publications

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“…Hayashi et al 39 proposed to combine rule extraction algorithm and sampling selection technique to achieve interpretable and accurate classification rules for PID data set. Similarly, Chakraborty et al 40 proposed the eclectic rule extraction from neural network recursively (ERENNR) algorithm, which generated rules from dataset with mixed attributes in the guise of attribute data ranges.…”

Section: Related Workmentioning

confidence: 99%

Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes

Hao

Luo

Pan

2022

Sci Rep

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Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been applied to increase the diagnosis rate, but most of them are black boxes lacking interpretability. Rule extraction is usually used to turn on the black box. As the number of diabetic patients is far less than that of healthy people, the rules obtained by the existing rule extraction methods tend to identify healthy people rather than diabetic patients. To address the problem, a method for extracting reduced rules based on biased random forest and fuzzy support vector machine is proposed. Biased random forest uses the k-nearest neighbor (k-NN) algorithm to identify critical samples and generates more trees that tend to diagnose diabetes based on critical samples to improve the tendency of the generated rules for diabetic patients. In addition, the conditions and rules are reduced based on the error rate and coverage rate to enhance interpretability. Experiments on the Diabetes Medical Examination Data collected by Beijing Hospital (DMED-BH) dataset demonstrate that the proposed approach has outstanding results (MCC = 0.8802) when the rules are similar in number. Moreover, experiments on the Pima Indian Diabetes (PID) and China Health and Nutrition Survey (CHNS) datasets prove the generalization of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes

Hao

Luo

Pan

2022

Sci Rep

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“…However, researchers found a solution to this problem in the form of rule extraction. The rule extraction is a process to represent the knowledge learned by NNs in the form of symbolic classi cation rules ([6], [7]). Many works exist to extract classi cation rules from NNs, but most of them extract rules from individual NNs.…”

Section: Introductionmentioning

confidence: 99%

Rule extraction using ensemble of neural network ensembles

Chakraborty¹,

Biswas²,

Purkayastha³

2022

Cognitive Systems Research

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“…Andrews et al reviewed the main neural network rule extraction approaches [3]. According to their survey, the neural network rule extraction methods could be divided into two categories, that is, methods by structural analysis and that of using performance analysis [4,5]. The former includes the Gallant algorithm [6], Subset algorithm [7] and M of the following N(MOFN) algorithm [8], etc., while the latter has Rule from Facts (RF) algorithm, Rule from Networks (RN) algorithm [9] and Benitez algorithm [10], etc.…”

Section: Introductionmentioning

confidence: 99%

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Zhou

et al. 2020

IEEE Access

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Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmony search algorithm (AGOHS) is proposed to overcome the traditional harmony search algorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy.

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Rule Extraction from Neural Network Using Input Data Ranges Recursively

Cited by 20 publications

References 19 publications

Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes

Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes

Rule extraction using ensemble of neural network ensembles

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

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