Fuzzy rule extraction using hybrid evolutionary models for data mining systems

2011 ◽  
Author(s):  
Ilnaz Edalat ◽  
Mohammad Saniee Abadeh ◽  
Mohammad Teshnehlab ◽  
Ali Nayyerirad
Keyword(s):  
Data Mining ◽  
Fuzzy Rule ◽  
Rule Extraction ◽  
Evolutionary Models

Data Discovery Approaches for Vague Spatial Data

Data Mining ◽  
2013 ◽  
pp. 50-65
Author(s):  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Rough Set ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Spatial Databases ◽  
Uncertain Data ◽  
Rule Extraction ◽  
Data Discovery

This chapter focuses on the application of the discovery of association rules in approaches vague spatial databases. The background of data mining and uncertainty representations using rough set and fuzzy set techniques is provided. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets is described. Finally, an example of rule extraction for both types of uncertainty representations is given.

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.

Introduction to Fuzzy Data Mining Methods

2011 ◽  
pp. 55-95 ◽  
Author(s):  
Balazs Feil ◽  
Janos Abonyi
Keyword(s):  
Data Mining ◽  
Model Performance ◽  
Fuzzy Rule ◽  
Classification Problem ◽  
Data Sets ◽  
Rule Based ◽  
Data Mining Algorithms ◽  
Rule Bases ◽  
Mining Algorithms ◽  
Extraction Model

This chapter aims to give a comprehensive view about the links between fuzzy logic and data mining. It will be shown that knowledge extracted from simple data sets or huge databases can be represented by fuzzy rule-based expert systems. It is highlighted that both model performance and interpretability of the mined fuzzy models are of major importance, and effort is required to keep the resulting rule bases small and comprehensible. Therefore, in the previous years, soft computing based data mining algorithms have been developed for feature selection, feature extraction, model optimization, and model reduction (rule based simplification). Application of these techniques is illustrated using the wine data classification problem. The results illustrate that fuzzy tools can be applied in a synergistic manner through the nine steps of knowledge discovery.

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