The full text of this article
Mining manufacturing data using genetic algorithm-based feature set decomposition
by Lior Rokach
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 4, No. 1/2, 2008
Abstract: Data mining methods can be used for discovering interesting patterns in manufacturing databases. These patterns can be used to improve manufacturing processes. However, data accumulated in manufacturing plants usually suffer from the 'Curse of Dimensionality', that is, relatively small number of records compared to large number of input features. As a result, conventional data mining methods may be inaccurate in these cases. This paper presents a new feature set decomposition approach that is based on genetic algorithm. For this purpose a new encoding schema is proposed and its properties are discussed. Moreover we examine the effectiveness of using a Vapnik-Chervonenkis dimension bound for evaluating the fitness function of multiple oblivious trees classifiers. The new algorithm was tested on various real-world manufacturing data sets. The results obtained have been compared to other methods, indicating the superiority of the proposed algorithm.
Online publication date: Sat, 22-Dec-2007
is only available to individual subscribers or to users at subscribing institutions.
Go to Inderscience Online Journals to access the Full Text of this article.
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Systems Technologies and Applications (IJISTA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable).
See our Orders page to subscribe.
If you still need assistance, please email email@example.com