Regularized F-Measure Maximization for Feature Selection and Classification
Joint Authors
Jiang, Feng
Liu, Zhenqiu
Tan, Ming
Source
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-04-27
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Receiver Operating Characteristic (ROC) analysis is a common tool for assessing the performance of various classifications.
It gained much popularity in medical and other fields including biological markers and, diagnostic test.
This is particularly due to the fact that in real-world problems misclassification costs are not known, and thus, ROC curve and related utility functions such as F-measure can be more meaningful performance measures.
F-measure combines recall and precision into a global measure.
In this paper, we propose a novel method through regularized F-measure maximization.
The proposed method assigns different costs to positive and negative samples and does simultaneous feature selection and prediction with L1 penalty.
This method is useful especially when data set is highly unbalanced, or the labels for negative (positive) samples are missing.
Our experiments with the benchmark, methylation, and high dimensional microarray data show that the performance of proposed algorithm is better or equivalent compared with the other popular classifiers in limited experiments.
American Psychological Association (APA)
Liu, Zhenqiu& Tan, Ming& Jiang, Feng. 2009. Regularized F-Measure Maximization for Feature Selection and Classification. BioMed Research International،Vol. 2009, no. 2009, pp.1-8.
https://search.emarefa.net/detail/BIM-988411
Modern Language Association (MLA)
Liu, Zhenqiu…[et al.]. Regularized F-Measure Maximization for Feature Selection and Classification. BioMed Research International No. 2009 (2009), pp.1-8.
https://search.emarefa.net/detail/BIM-988411
American Medical Association (AMA)
Liu, Zhenqiu& Tan, Ming& Jiang, Feng. Regularized F-Measure Maximization for Feature Selection and Classification. BioMed Research International. 2009. Vol. 2009, no. 2009, pp.1-8.
https://search.emarefa.net/detail/BIM-988411
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-988411