Rule Extraction from Trained Neural Network with Evolutionary Algorithms

2003 ◽  
pp. 370-375 ◽  
Author(s):  
Urszula Markowska-Kaczmar ◽  
Marcin Chumieja
Keyword(s):  
Neural Network ◽  
Evolutionary Algorithms ◽  
Rule Extraction ◽  
Trained Neural Network

Rule Extraction from Training Data Using Neural Network

2016 ◽  
Vol 26 (03) ◽  
pp. 1750006 ◽  
Author(s):  
Saroj Kumar Biswas ◽  
Manomita Chakraborty ◽  
Biswajit Purkayastha ◽  
Pinki Roy ◽  
Dalton Meitei Thounaojam
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
High Performance ◽  
Rule Extraction ◽  
Black Box ◽  
Training Data ◽  
Extraction Algorithm ◽  
Artificial Neural ◽  
Trained Neural Network

Data Mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. One of the most commonly used techniques in data mining is Artificial Neural Network due to its high performance in many application domains. Despite many advantages of Artificial Neural Network, one of its main drawbacks is its inherent black box nature which is the main problem of using Artificial Neural Network in data mining. Therefore, this paper proposes a rule extraction algorithm from neural network using classified and misclassified data to convert the black box nature of Artificial Neural Network into a white box. The proposed algorithm is a modification of the existing algorithm, Rule Extraction by Reverse Engineering (RxREN). The proposed algorithm extracts rules from trained neural network for datasets with mixed mode attributes using pedagogical approach. The proposed algorithm uses both classified as well as misclassified data to find out the data ranges of significant attributes in respective classes, which is the innovation of the proposed algorithm. The experimental results clearly show that the performance of the proposed algorithm is superior to existing algorithms.

Fuzzy Rule Acquisition from Trained Artificial Neural Networks

1999 ◽  
Vol 3 (5) ◽  
pp. 357-367
Author(s):  
Peter Geczy ◽  
◽  
Shiro Usui
Keyword(s):  
Neural Network ◽  
Fuzzy Rule ◽  
Rule Extraction ◽  
Fuzzy Rules ◽  
Data Set ◽  
Rule Acquisition ◽  
Extraction Algorithm ◽  
Artificial Neural ◽  
Trained Neural Network ◽  
Fixed Structure

We approach the problem of rule extraction in its primary form. That is, given a trained artificial neural network, we extract rules classifying data set as correctly as possible. Attention is oriented toward extraction of fuzzy rules. The choice of fuzzy rules underlines the aim of balancing rule comprehensibility and complexity. To achieve higher comprehensibility of extracted rules, the formulated theoretical material is an extension of crisp rule extraction 1). A rule extraction algorithm is introduced. The presented algorithm for fuzzy rule extraction implies from the derived theoretical results rather than from heuristics. The rule extraction algorithm incorporates a ’built-in’ rule simplification mechanism. This feature is beneficial in cases when trained neural network structure is overdetermined for a given task. The rule extraction algorithm is experimentally demonstrated. Demonstrations incorporate both structure modification training and fixed structure training.

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

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