NDLI: Feature Ordering for Neural Incremental Attribute Learning Based on Fisher's Linear Discriminant

Content Provider	IEEE Xplore Digital Library
Author	Ting Wang Sheng-Uei Guan
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Software Eng., Xi'an Jiaotong-Liverpool Univ., Suzhou, China (Sheng-Uei Guan) \|\| Dept. of Comput. Sci., Univ. of Liverpool, Liverpool, UK (Ting Wang)
Abstract	Incremental attribute learning (IAL) often gradually imports and trains pattern features in one or more size, which makes feature ordering become a novel preprocessing work in IAL process. In previous studies, the calculation of feature ordering is often Based on feature's single contribution to outputs, which is similar to wrapper methods in feature selection. However, such a process is time-consuming. In this paper, a new approach for feature ordering is presented, where feature ordering is ranked by Fisher Score, a metric derived by Fisher's Linear Discriminant (FLD). Based on neural network IAL model, experimental results verified that feature ordering derived by Fisher Score can not only save time, but also obtain the best classification rate compared with those in previous studies.
Sponsorship	IEEE Nanjing Sect. CIS Chapt.
Starting Page	507
Ending Page	510
File Size	267077
Page Count	4
File Format	PDF
ISBN	9780769550114
e-ISBN	9780769550114
DOI	10.1109/IHMSC.2013.268
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Error analysis Feature ordering Fisher's Linear Discriminant Neural networks Pattern classification Glass Pattern recognition Diabetes Intelligent systems Incremental attribute learning
Content Type	Text
Resource Type	Article

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