A feature selection method using a fuzzy mutual information measure Online publication date: Mon, 30-Aug-2010
by Maria Del Rosario Suarez, Jose R. Villar, Javier Grande
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 2, No. 2, 2010
Abstract: Attempting to obtain a classifier or a model from datasets could be a cumbersome task, specifically when using datasets of high dimensionality. The larger the amount of features the higher the complexity of the problem and the longer the time that is expended in generating the outcome (the classifier or the model). Feature selection has been proved as a good technique for choosing features that best describe the system under certain criteria or measure. There are several different approaches for feature selection, but to our knowledge, there are not many different approaches when feature selection is involved with imprecise data and genetic fuzzy systems. In this paper, a feature selection method based on the fuzzy mutual information is proposed. The outlined method is valid for classifying problems when expertise partitioning is given, and it represents the base of future work including the use of imprecise data.
Online publication date: Mon, 30-Aug-2010
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 Reasoning-based Intelligent Systems (IJRIS):
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 firstname.lastname@example.org