Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Meta Analysis of Classification Algorithms for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
On Classifier Domains of Competence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Multi-class pattern classification using neural networks
Pattern Recognition
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary Computation
Free lunches for function and program induction
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Genetic Programming and Evolvable Machines
There Is a Free Lunch for Hyper-Heuristics, Genetic Programming and Computer Scientists
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
A comprehensive view of fitness landscapes with neutrality and fitness clouds
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Measuring bloat, overfitting and functional complexity in genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A fine-grained view of GP locality with binary decision diagrams as ant phenotypes
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Predicting problem difficulty for genetic programming applied to data classification
Proceedings of the 13th annual conference on Genetic and evolutionary computation
How many neurons?: a genetic programming answer
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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A fundamental task that must be addressed before classifying a set of data, is that of choosing the proper classification method. In other words, a researcher must infer which classifier will achieve the best performance on the classification problem in order to make a reasoned choice. This task is not trivial, and it is mostly resolved based on personal experience and individual preferences. This paper presents a methodological approach to produce estimators of classifier performance, based on descriptive measures of the problem data. The proposal is to use Genetic Programming (GP) to evolve mathematical operators that take as input descriptors of the problem data, and output the expected error that a particular classifier might achieve if it is used to classify the data. Experimental tests show that GP can produce accurate estimators of classifier performance, by evaluating our approach on a large set of 500 two-class problems of multimodal data, using a neural network for classification. The results suggest that the GP approach could provide a tool that helps researchers make a reasoned decision regarding the applicability of a classifier to a particular problem.