Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic Programming for Multiple Class Object Detection
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Towards Genetic Programming for Texture Classification
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Open BEAGLE: A New C++ Evolutionary Computation Framework
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic Programming for data classification: partitioning the search space
Proceedings of the 2004 ACM symposium on Applied computing
Genetic Programming and Evolvable Machines
Expert Systems with Applications: An International Journal
IEEE Transactions on Evolutionary Computation
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
The influence of mutation on population dynamics in multiobjective genetic programming
Genetic Programming and Evolvable Machines
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
A novel approach to design classifiers using genetic programming
IEEE Transactions on Evolutionary Computation
Validation sets for evolutionary curtailment with improved generalisation
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Exploring boundaries: optimising individual class boundaries for binary classification problem
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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This paper describes a technique which can be used with Genetic Programming (GP) to reduce implicit bias in binary classification tasks. Arbitrarily chosen class boundaries can introduce bias, but if individuals can choose their own boundaries, tailored to their function set, then their outputs are automatically scaled into a suitable range. These boundaries evolve over time as the individuals adapt to the data. Our system calculates the Evolved Class Boundary(ECB) for each individual in every generation, with the twin aims of reducing training times and improving test fitness. The method is tested on three benchmark binary classification data sets from the medical domain. The results obtained suggest that the strategy can improve training, validation and test fitness, and can also result in smaller individuals as well as reduced training times. Our approach is compared with a standard benchmark GP system, as well as with over twenty other systems from the literature, many of which use highly tuned, non-EC methods, and is shown to yield superior results in many cases.