C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Rule extraction from trained neural networks using genetic algorithms
Proceedings of the second world congress on Nonlinear analysts: part 3
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Integrating membership functions and fuzzy rule sets from multiple knowledge sources
Fuzzy Sets and Systems
Machine Learning
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Data Mining Using Grammar-Based Genetic Programming and Applications
Data Mining Using Grammar-Based Genetic Programming and Applications
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
The GA-P: A Genetic Algorithm and Genetic Programming Hybrid
IEEE Expert: Intelligent Systems and Their Applications
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Data Mining and Knowledge Discovery in Databases: Implications for Scientific Databases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
SCCC '00 Proceedings of the XX International Conference of the Chilean Computer Science Society
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Mining multiple comprehensible classification rules using genetic programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Automatically integrating multiple rule sets in adistributed-knowledge environment
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A distributed evolutionary classifier for knowledge discovery in data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Interference-less neural network training
Neurocomputing
Rule Extraction from Neural Networks Via Ant Colony Algorithm for Data Mining Applications
Learning and Intelligent Optimization
Generating prediction rules for liquefaction through data mining
Expert Systems with Applications: An International Journal
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
Expert Systems with Applications: An International Journal
An evolutionary memetic algorithm for rule extraction
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
A memetic approach for the knowledge extraction
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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This paper presents a dual-objective evolutionary algorithm (DOEA) for extracting multiple decision rule lists in data mining, which aims at satisfying the classification criteria of high accuracy and ease of user comprehension. Unlike existing approaches, the algorithm incorporates the concept of Pareto dominance to evolve a set of non-dominated decision rule lists each having different classification accuracy and number of rules over a specified range. The classification results of DOEA are analyzed and compared with existing rule-based and non-rule based classifiers based upon 8 test problems obtained from UCI Machine Learning Repository. It is shown that the DOEA produces comprehensible rules with competitive classification accuracy as compared to many methods in literature. Results obtained from box plots and t-tests further examine its invariance to random partition of datasets.