Feature selection for gene function prediction using multi-labelled lazy learning
by Yu-Hai Liu, Guo-Zheng Li, Hong-Yu Zhang, Mary Qu Yang, Jack Y. Yang
International Journal of Functional Informatics and Personalised Medicine (IJFIPM), Vol. 1, No. 3, 2008

Abstract: In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, feature selection for the multi-label method was proposed based on mutual information. In detail, we use the distribution of mutual information for feature selection in the multi-label problems. Our experiment was preceded on a multi-label lazy learning approach named ML-kNN, which is derived from the traditional k-Nearest Neighbour (KNN) algorithm. Experimental results on a real-world multi-label bioinformatics data show that ML-kNN with feature selection greatly outperforms the prior ML-kNN algorithm.

Online publication date: Sat, 22-Nov-2008

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
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 Functional Informatics and Personalised Medicine (IJFIPM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your 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 subs@inderscience.com