Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization

In multilabel 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, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., backprop...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 18; no. 10; pp. 1338 - 1351
Main Authors: ZHANG, Min-Ling, ZHOU, Zhi-Hua
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.10.2006
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Back propagation
backpropagation
Backpropagation algorithms
Biochemistry
Bioinformatics
Computer science; control theory; systems
Connectionism. Neural networks
Data mining
Error functions
Exact sciences and technology
functional genomics
Genomics
Government
Information systems. Data bases
Labels
Layout
Learning
Learning systems
Machine learning
Memory organisation. Data processing
multilabel learning
Neural networks
Software
Tasks
Text categorization
Texts
Genomics
neural networks
text categorization
Information extraction
functional genomics
multilabel learning
Neural network
Data mining
Backpropagation algorithm
Machine learning
backpropagation
Linguistics
Learning algorithm
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:In multilabel 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, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., backpropagation for multilabel learning, is proposed. It is derived from the popular backpropagation algorithm through employing a novel error function capturing the characteristics of multilabel learning, i.e., the labels belonging to an instance should be ranked higher than those not belonging to that instance. Applications to two real-world multilabel learning problems, i.e., functional genomics and text categorization, show that the performance of BP-MLL is superior to that of some well-established multilabel learning algorithms
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2006.162