The nature of statistical learning theory
The nature of statistical learning theory
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Design and Analysis of Experiments
Design and Analysis of Experiments
Discovering the Mysteries of Neural Networks
International Journal of Hybrid Intelligent Systems
Support vector machines combined with feature selection for breast cancer diagnosis
Expert Systems with Applications: An International Journal
Decompositional Rule Extraction from Support Vector Machines by Active Learning
IEEE Transactions on Knowledge and Data Engineering
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
Expert Systems with Applications: An International Journal
Mixture classification model based on clinical markers for breast cancer prognosis
Artificial Intelligence in Medicine
Support vector regression based hybrid rule extraction methods for forecasting
Expert Systems with Applications: An International Journal
Genetic rule extraction optimizing brier score
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Explanation and reliability of prediction models: the case of breast cancer recurrence
Knowledge and Information Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Visual Analytics for model-based medical image segmentation: Opportunities and challenges
Expert Systems with Applications: An International Journal
A hybrid intelligent system for medical data classification
Expert Systems with Applications: An International Journal
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Machine learning support for medical decision making is truly helpful only when it meets two conditions: high prediction accuracy and a good explanation of how the diagnosis was reached. Support vector machines (SVMs) successfully achieve the first target due to a kernel-based engine; evolutionary algorithms (EAs) can greatly accomplish the second owing to their adaptable nature. In this context, the current paper puts forward a two-step hybridized methodology, where learning is accurately performed by the SVMs and a comprehensible emulation of the resulting decision model is generated by EAs in the form of propositional rules, while referring only those indicators that highly influence the class separation. An individual highlighting of the medical attributes that trigger a specific diagnosis for a current patient record is additionally obtained; this feature thus increases the confidence of the physician in the resulting automated diagnosis. Without loss of generality, we aim to model three breast cancer instances, for reasons of both high incidence of the disease and the large application of state of the art artificial intelligence methods for this medical task. As such, the prediction of a benign/malignant condition as well as the recurrence/nonrecurrence of a cancer event are studied on the Wisconsin corresponding data sets from the UCI Machine Learning Repository. The proposed hybridization reached its goals. Rule prototypes evolve against a SVM consistent training data, while diversity among the different classes is implicitly preserved. Feature selection eventually leads to a resulting rule set where only the significant medical indicators together with the discriminating threshold values are referred, while individual relevance of attributes can be additionally obtained for each patient. The gain is thus dual: the EA benefits from a noise-free SVM preprocessed data and the resulting SVM model is able to output rules in a comprehensible, concise format for the physician.