Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Default hierarchy formation and memory exploitation in learning classifier systems
Default hierarchy formation and memory exploitation in learning classifier systems
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Automated alphabet reduction method with evolutionary algorithms for protein structure prediction
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Mutual information neuro-evolutionary system (MINES)
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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This paper studies the performance of a newly developed supervised Michigan-style learning classifier system (LCS), called MILCS, on protein structure prediction problems and our observation of its default hierarchies (DHs). We present experimental results, and contrast them to results from other machine learning systems, named XCS, UCS, GAssist, BioHEL, C4.5 and Naïve Bayes. We use our technique for visualizing explanatory power of the resulting rule sets and their hierarchical structure. Final comments include future directions for this research, including investigations in neural networks and other systems.