Exploring the Power of Genetic Search in Learning Symbolic Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Multiple Learning Strategies in First Order Logics
Machine Learning - Special issue on multistrategy learning
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Learning Classifier Systems, From Foundations to Applications
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Handbook of data mining and knowledge discovery
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
CLIP4: hybrid inductive machine learning algorithm that generates inequality rules
Information Sciences: an International Journal - Special issue: Soft computing data mining
An integrated approach for operational knowledge acquisition of refuse incinerators
Expert Systems with Applications: An International Journal
Using coverage as a model building constraint in learning classifier systems
Evolutionary Computation
Search-intensive concept induction
Evolutionary Computation
An analysis of the “universal suffrage” selection operator
Evolutionary Computation
Short communication: Mining knowledge from data using Anticipatory Classifier System
Knowledge-Based Systems
An Evolutionary Ensemble-Based Method for Rule Extraction with Distributed Data
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Evolutionary Extraction of Association Rules: A Preliminary Study on their Effectiveness
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Connectionist theory refinement: genetically searching the space of network topologies
Journal of Artificial Intelligence Research
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
People detection through quantified fuzzy temporal rules
Pattern Recognition
Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Efficient Distributed Genetic Algorithm for Rule extraction
Applied Soft Computing
COGIN: symbolic induction with genetic algorithms
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
IEEE Transactions on Evolutionary Computation
Evolutionary generation of prototypes for a learning vector quantization classifier
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Artificial Intelligence in Medicine
reCORE --- a coevolutionary algorithm for rule extraction
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Learning Classification Programs: The Genetic Algorithm Approach
Fundamenta Informaticae
An interpretable classification rule mining algorithm
Information Sciences: an International Journal
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Symbolic induction is a promising approach to constructing decision models by extracting regularities from a data set of examples. The predominant type of model is a classification rule (or set of rules) that maps a set of relevant environmental features into specific categories or values. Classifying loan risk based on borrower profiles, consumer choice from purchase data, or supply levels based on operating conditions are all examples of this type of model-building task. Although current inductive approaches, such as ID3 and CN2, perform well on certain problems, their potential is limited by the incremental nature of their search. Genetic algorithms (GA) have shown great promise on complex search domains, and hence suggest a means for overcoming these limitations. However, effective use of genetic search in this context requires a framework that promotes the fundamental model-building objectives of predictive accuracy and model simplicity. In this article we describe COGIN, a GA-based inductive system that exploits the conventions of induction from examples to provide this framework. The novelty of COGIN lies in its use of training set coverage to simultaneously promote competition in various classification niches within the model and constrain overall model complexity. Experimental comparisons with NewID and CN2 provide evidence of the effectiveness of the COGIN framework and the viability of the GA approach.