Rule Extraction From Neuro-fuzzy System for Classification Using Feature Weights

Singh, Heisnam Rohen; Biswas, Saroj

doi:10.4018/ijfsa.2020040103

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Benitez et al [20] proved that multi-layer feed-forward NN is equivalent to a fuzzy system so that NN can be interpreted by its corresponding fuzzy rules of the fuzzy system. Singh and Biswas [21] proposed a fuzzy neural system for feature selection and classification. It can determine a large number of linguistic features for each input and use the importance of input features and the certainty of rules to extract understandable rules easily.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Knowledge Extraction Framework Using Modified Adaptive Genetic Algorithm and BPNN

Ding

et al. 2022

IEEE Access

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Fault diagnosis based on the expert system (ES) is still a research topic of manufacturing in the Industry 4.0 because of the stronger interpretability. As the core component of the ES, fault diagnosis accuracy is positively correlated to the precise of the knowledge base. But it is difficult for users to understand the knowledge obtained from the original dataset utilizing the existing knowledge extraction method. Therefore, it is of great significance to extract easy-to-understand and exact rules from the NN framework. This paper proposes a hybrid extraction framework to perform the rule extraction for overcoming this drawback. First, an improved adaptive genetic algorithm (GA) using a logistic function, namely LAGA, is proposed to solve the traditional GA's insufficient prediction performance issue. Compared with the other three mainstream adaptive GAs, the experiment results of optimizing six selected test functions by these GA variants show that the LAGA algorithm's convergence accuracy and speed have been greatly improved, especially for high latitude functions. On this basis, a rule extraction method based on symbol rule and NN, namely the LAGA-BP framework, is discussed in this manuscript to classify the real-valued attributes. This framework obtains hidden knowledge (knowledge refinement process) by NN and further transforms the acquired hidden knowledge into more easy-to-understand rule knowledge (rule extraction process). The execution of the LAGA-BP framework could be separated into two phases. The first phase is to optimize a back propagation NN (BPNN) using the LAGA and refine prediction classification knowledge over the optimized BPNN. In the second phase, an attribute reduction algorithm using multi-layered NN (SD algorithm) based on two different superposed networks is used on this framework to reduce data-set attributes and then uses the K-means clustering algorithm to extract the ifthen rule from the simplified attributes. the Wisconsin breast cancer data-set is used as a case study to reveal the correctness and robustness of the proposed LAGA-BP method. Consulting relevant medical personnel and referencing relevant data shows that the rules extracted using this method help verify the diagnosis results, thus verifying the proposed framework's feasibility and practicality.

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Section: Introductionmentioning

confidence: 99%