Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
An introduction to genetic algorithms
An introduction to genetic algorithms
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms in Management Applications
Evolutionary Algorithms in Management Applications
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
C4.5: Programs for Machine Learning
C4.5: Programs for Machine Learning
Interactive Evolutionary Computation as Humanized Computational Intelligence Technology
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Hybrid learning using genetic algorithms and decision trees for pattern classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Investigation of rule interestingness in medical data mining
AM'03 Proceedings of the Second international conference on Active Mining
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Interactive evolutionary computation (IEC) is a subjective and interactive method to evaluate the qualities of offspring generated by genetic operations. Data mining, an interdisciplinary research area including artificial intelligence, statistics and databases, is a series of semi-automated processes to extract explicit useful knowledge from given databases. In this chapter, we adopt IEC in order to select relevant features in inductive learning for data mining tasks. The method we have proposed is used to discover efficient decision knowledge from noisy clinical data in a medical domain. This chapter describes the principles of IEC and SIBILE (Simulated Breeding and Inductive Learning), which we have developed for practical data mining problems, and its application to a common data set on clinical patients, The basic ideas of SIBILIE are that IEC is used to get the effective feature, from the data and that inductive learning is used to acquire simple decision rules from the subset of the data.