scholarly journals Logical Rule Extraction Using a Learning Machine with Sparse Function Representation.

2003 ◽  
Vol 10 (3) ◽  
pp. 139-146
Author(s):  
Taichi Hayasaka ◽  
Shiro Usui
Keyword(s):  
Rule Extraction ◽  
Function Representation ◽  
Logical Rule ◽  
Learning Machine

scholarly journals An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3535 ◽  
Author(s):  
Wang ◽  
Li ◽  
Zhang ◽  
Zou
Keyword(s):  
Decision Making ◽  
Liquid Steel ◽  
Classification Problem ◽  
Rule Extraction ◽  
Black Box ◽  
Production Data ◽  
Integrated Methodology ◽  
Irrelevant Attributes ◽  
Learning Machine ◽  
Elm Classifier

The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher than its operating restriction, within the operation restriction and lower than the operating restriction. Based on these black-box model results, an integrated three-step approach for rule extraction is constructed to interpret the understandability of the proposed ELM classifier. First, the irrelevant attributes are pruned without decreasing the classification accuracy. Second, fuzzy rules are generated in the form of discrete input attributes and the target classification. Last but not the least, the rules are refined by generating rules with continuous attributes. The novelty of the proposed rule extraction approach lies in the generation of rules using the discrete and continuous attributes at different stages. The proposed method is analyzed and validated on actual production data derived from a No.2 steelmaking workshop in Baosteel. The experimental results revealed that the extracted rules are effective for the VTD system in classifying the end temperature of liquid steel into high, normal, and low ranges. In addition, much fewer input attributes are needed to implement the rules for the manufacturing process of VTD. The extracted rules serve explicit instructions for decision-making for the VTD operators.

Fuzzy and Crisp Logical Rule Extraction Methods in Application to Medical Data

2000 ◽  
pp. 593-616 ◽  
Author(s):  
Włodzisław Duch ◽  
Rafał Adamczak ◽  
Krzysztof Grąbczewski ◽  
Grzegorz Żal ◽  
Yoichi Hayashi
Keyword(s):  
Extraction Methods ◽  
Rule Extraction ◽  
Medical Data ◽  
Logical Rule

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

2015 ◽  
Vol 67 ◽  
pp. 138-144 ◽  
Author(s):  
Xinzheng Xu ◽  
Guanying Wang ◽  
Shifei Ding ◽  
Xiangying Jiang ◽  
Zuopeng Zhao
Keyword(s):  
Neural Networks ◽  
Extreme Learning Machine ◽  
Rule Extraction ◽  
New Method ◽  
Learning Machine ◽  
Granular Neural Networks

Logical rule models of classification response times

2009 ◽  
Author(s):  
Robert M. Nosofsky ◽  
Mario Fific
Keyword(s):  
Response Times ◽  
Logical Rule
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