Neural Networks
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural networks and the bias/variance dilemma
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Knowledge-based artificial neural networks
Artificial Intelligence
Fuzzy engineering
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence Review - Special issue on lazy learning
Data preparation for data mining
Data preparation for data mining
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Bump hunting in high-dimensional data
Statistics and Computing
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Obtaining Simplified Rule Bases by Hybrid Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Simplifying decision trees: A survey
The Knowledge Engineering Review
Neural Networks And Hybrid Intelligent Models: Foundations, Theory, And Applications
IEEE Transactions on Neural Networks
Decision trees can initialize radial-basis function networks
IEEE Transactions on Neural Networks
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Since there is no individual approach that can be universally applied to effectively solve the hard problems of artificial intelligence and data analysis, hybrid systems are necessary to better tackle specific tasks by exploiting the advantages of different methodologies in a single framework. Based on known results of combining neural networks and rule-based systems, this work presents a hybrid system with the purpose of simplifying rule sets obtained from rule induction algorithms on classification problems without increasing the accuracy error. This is motivated by assuming that simplicity can lead to more understandable models and rule induction algorithms often provide an excessive number of rules necessary to classify future examples within a given accuracy error, even after pruning. Experimental evidence suggests effective gains on a benchmark of sixteen data sets. Experiments were also performed to detect the effect of different components of the proposed approach in achieving the results and so helping to explain why this hybrid system works.