C4.5: programs for machine learning
C4.5: programs for machine learning
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
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
Bounding the effect of noise in multiobjective learning classifier systems
Evolutionary Computation
For real! XCS with continuous-valued inputs
Evolutionary Computation
The compact classifier system: motivation, analysis, and first results
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Fast rule matching for learning classifier systems via vector instructions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Validity of the single processor approach to achieving large scale computing capabilities
AFIPS '67 (Spring) Proceedings of the April 18-20, 1967, spring joint computer conference
Data mining in learning classifier systems: comparing XCS with GAssist
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Fast rule representation for continuous attributes in genetics-based machine learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Information Sciences: an International Journal
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Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types--a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique-- $${\tt NAX}$$ --based on an incremental genetics-based rule learner. $${\tt NAX}$$ exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that $${\tt NAX}$$ is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.