Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Fast rule matching for learning classifier systems via vector instructions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Automated alphabet reduction method with evolutionary algorithms for protein structure prediction
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An analysis of matching in learning classifier systems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
A method for handling numerical attributes in GA-based inductive concept learners
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Speeding up the evaluation of evolutionary learning systems using GPGPUs
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Analysing bioHEL using challenging boolean functions
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Post-processing operators for decision lists
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Datasets with a large number of attributes are a difficult challenge for evolutionary learning techniques. The recently proposed attribute list rule representation has shown to be able to significantly improve the overall performance (e.g. run-time, accuracy, rule set size) of the BioHEL Iterative Evolutionary Rule Learning system. In this paper we, first, extend the attribute list rule representation so it can handle not only continuous domains, but also datasets with a very large number of mixed discrete-continuous attributes. Secondly, we benchmark the new representation with a diverse set of large-scale datasets and, third, we compare the new algorithms with several well-known machine learning methods. The experimental results we describe in the paper show that the new representation is equal or better than the state of-the-art in evolutionary rule representations both in terms of the accuracy obtained with the benchmark datasets used, as well as in terms of the computational time requirements needed to achieve these improved accuracies. The new attribute list representation puts BioHEL on an equal footing with other well-established machine learning techniques in terms of accuracy. In the paper, we also analyse and discuss the current weaknesses behind the current representation and indicate potential avenues for correcting them.