Evolutionary Algorithms in Management Applications
巻, 号, ページ
pp. 211-224
出版年月
1995年4月
出版者
和文:
英文:
Springer-Verlag New York
会議名称
和文:
英文:
開催地
和文:
英文:
アブストラクト
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.
各種検索
サポート
ヘルプ