scholarly journals Classification Learning and Rule Extraction Systems using Extended Genetic Programming for Data Mining

2003 ◽  
Vol 15 (1) ◽  
pp. 84
Keyword(s):  
Data Mining ◽  
Genetic Programming ◽  
Rule Extraction ◽  
Classification Learning

A New Approach to the Extraction of ANN Rules and to Their Generalization Capacity Through GP

2004 ◽  
Vol 16 (7) ◽  
pp. 1483-1523 ◽  
Author(s):  
Juan R. Rabuñal ◽  
Julián Dorado ◽  
Alejandro Pazos ◽  
Javier Pereira ◽  
Daniel Rivero
Keyword(s):  
Genetic Programming ◽  
Rule Extraction ◽  
Human Beings ◽  
Activation Functions ◽  
New Approach ◽  
Previous Algorithm ◽  
Internal Distribution

Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.

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.

scholarly journals Improved model reduction and tuning of fractional-order controllers for analytical rule extraction with genetic programming

2012 ◽  
Vol 51 (2) ◽  
pp. 237-261 ◽  
Author(s):  
Saptarshi Das ◽  
Indranil Pan ◽  
Shantanu Das ◽  
Amitava Gupta
Keyword(s):  
Genetic Programming ◽  
Model Reduction ◽  
Fractional Order ◽  
Rule Extraction ◽  
Improved Model

Rule Extraction from Privacy Preserving Neural Network: Application to Banking

2011 ◽  
Vol 403-408 ◽  
pp. 920-928 ◽  
Author(s):  
Nekuri Naveen ◽  
V. Ravi ◽  
C. Raghavendra Rao
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Privacy Preservation ◽  
Cross Validation ◽  
Hybrid Approach ◽  
Rule Extraction ◽  
Privacy Preserving ◽  
Preservation Method ◽  
Network Application ◽  
Fold Cross Validation

In the last two decades in areas like banking, finance and medical research privacy policies restrict the data owners to share the data for data mining purpose. This issue throws up a new area of research namely privacy preserving data mining. In this paper, we proposed a privacy preservation method by employing Particle Swarm Optimization (PSO) trained Auto Associative Neural Network (PSOAANN). The modified (privacy preserved) input values are fed to a decision tree (DT) and a rule induction algorithm viz., Ripper for rule extraction purpose. The performance of the hybrid is tested on four benchmark and bankruptcy datasets using 10-fold cross validation. The results are compared with those obtained using the original datasets where privacy is not preserved. The proposed hybrid approach achieved good results in all datasets.

A GRAMMAR-GUIDED GENETIC PROGRAMMING FRAMEWORK CONFIGURED FOR DATA MINING AND SOFTWARE TESTING

2006 ◽  
Vol 16 (02) ◽  
pp. 245-267 ◽  
Author(s):  
SILVIA REGINA VERGILIO ◽  
AURORA POZO
Keyword(s):  
Data Mining ◽  
Genetic Programming ◽  
Software Testing ◽  
Relational Databases ◽  
Programming Framework ◽  
Induction Technique

Genetic Programming (GP) is a powerful software induction technique that can be applied to solve a wide variety of problems. However, most researchers develop tailor-made GP tools for solving specific problems. These tools generally require significant modifications in their kernel to be adapted to other domains. In this paper, we explore the Grammar-Guided Genetic Programming (GGGP) approach as an alternative to overcome such limitation. We describe a GGGP based framework, named Chameleon, that can be easily configured to solve different problems. We explore the use of Chameleon in two domains, not usually addressed by works in the literature: in the task of mining relational databases and in the software testing activity. The presented results point out that the use of the grammar-guided approach helps us to obtain more generic GP frameworks and that they can contribute in the explored domains.

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