Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Elements of information theory
Elements of information theory
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Knowledge discovery from data?
IEEE Intelligent Systems
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Discovery of Decision Rules from Databases: An Evolutionary Approach
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Interactive search of rules in medical data using multiobjective evolutionary algorithms
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Autonomous classifiers with understandable rule using multi-objective genetic algorithms
Expert Systems with Applications: An International Journal
An incremental genetic algorithm for classification and sensitivity analysis of its parameters
Expert Systems with Applications: An International Journal
Transactions on rough sets XII
A real coded MOGA for mining classification rules with missing attribute values
Proceedings of the 2011 International Conference on Communication, Computing & Security
Distributed learning with data reduction
Transactions on computational collective intelligence IV
A multi-objective genetic algorithm approach to rule mining for affective product design
Expert Systems with Applications: An International Journal
An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification
Journal of Systems and Software
Multi-Objective ant programming for mining classification rules
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
Expert Systems with Applications: An International Journal
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We present an elitist multi-objective genetic algorithm (EMOGA) for mining classification rules from large databases. We emphasize on predictive accuracy, comprehensibility and interestingness of the rules. However, predictive accuracy, comprehensibility and interestingness of the rules often conflict with each other. This makes it a multi-objective optimization problem that is very difficult to solve efficiently. We have proposed a multi-objective genetic algorithm with a hybrid crossover operator for optimizing these objectives simultaneously. We have compared our rule discovery procedure with simple genetic algorithm with a weighted sum of all these objectives. The experimental result confirms that our rule discovery algorithm has a clear edge over simple genetic algorithm.