%0 Conference Proceedings
%T Evolving Linear Discriminant in a Continuously Growing Dimensional Space for Incremental Attribute Learning
%+ Xi'an Jiaotong-Liverpool University [Suzhou]
%+ La Trobe University [Melbourne]
%A Wang, Ting
%A Guan, Sheng-Uei
%A Ting, T., O.
%A Man, Ka, Lok
%A Liu, Fei
%Z Part 12: DATICS
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 9th International Conference on Network and Parallel Computing (NPC)
%C Gwangju, South Korea
%Y James J. Park
%Y Albert Zomaya
%Y Sang-Soo Yeo
%Y Sartaj Sahni
%I Springer
%3 Network and Parallel Computing
%V LNCS-7513
%P 482-491
%8 2012-09-06
%D 2012
%R 10.1007/978-3-642-35606-3_57
%K pattern classification
%K incremental attribute learning
%K data preprocessing
%K feature ordering
%K neural networks
%Z Computer Science [cs]Conference papers
%X Feature Ordering is a unique preprocessing step in Incremental Attribute Learning (IAL), where features are gradually trained one after another. In previous studies, feature ordering derived based upon each individual feature’s contribution is time-consuming. This study attempts to develop an efficient feature ordering algorithm by some evolutionary approaches. The feature ordering algorithm presented in this paper is based on a criterion of maximum mean of feature discriminability. Experimental results derived by ITID, a neural IAL algorithm, show that such a feature ordering algorithm has a higher probability to obtain the lowest classification error rate with datasets from UCI Machine Learning Repository.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-01551374/document
%2 https://inria.hal.science/hal-01551374/file/978-3-642-35606-3_57_Chapter.pdf
%L hal-01551374
%U https://inria.hal.science/hal-01551374
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-7513