Imbalanced Datasets Classification by Fuzzy Rule Extraction and Genetic Algorithms

2006 ◽  
Author(s):  
Vicenc Soler ◽  
Jesus Cerquides ◽  
Josep Sabria ◽  
Jordi Roig ◽  
Marta Prim
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Rule ◽  
Rule Extraction ◽  
Imbalanced Datasets

Support Vector Machine based Hybrid Classifiers and Rule Extraction thereof

2010 ◽  
pp. 404-426 ◽  
Author(s):  
M. A.H. Farquad ◽  
V. Ravi ◽  
Raju S. Bapi
Keyword(s):  
Radial Basis Function Network ◽  
Hybrid Approach ◽  
Fuzzy Rule ◽  
Rule Extraction ◽  
Bankruptcy Prediction ◽  
Fuzzy Rules ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Support Vectors ◽  
Hybrid Classifiers

Support vector machines (SVMs) have proved to be a good alternative compared to other machine learning techniques specifically for classification problems. However just like artificial neural networks (ANN), SVMs are also black box in nature because of its inability to explain the knowledge learnt in the process of training, which is very crucial in some applications like medical diagnosis, security and bankruptcy prediction etc. In this chapter a novel hybrid approach for fuzzy rule extraction based on SVM is proposed. This approach handles rule-extraction as a learning task, which proceeds in two major steps. In the first step the authors use labeled training patterns to build an SVM model, which in turn yields the support vectors. In the second step extracted support vectors are used as input patterns to fuzzy rule based systems (FRBS) to generate fuzzy “if-then” rules. To study the effectiveness and validity of the extracted fuzzy rules, the hybrid SVM+FRBS is compared with other classification techniques like decision tree (DT), radial basis function network (RBF) and adaptive network based fuzzy inference system. To illustrate the effectiveness of the hybrid developed, the authors applied it to solve a bank bankruptcy prediction problem. The dataset used pertain to Spanish, Turkish and US banks. The quality of the extracted fuzzy rules is evaluated in terms of fidelity, coverage and comprehensibility.

Rule extraction from trained neural networks using genetic algorithms

1997 ◽  
Vol 30 (3) ◽  
pp. 1639-1648 ◽  
Author(s):  
A. Duygu Arbatli ◽  
H. Levent Akin
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Rule Extraction
Sign in / Sign up

Export Citation Format

Share Document