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Incremental Neural Network Training for Medical Diagnosis
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Author(s): Sheng-Uei Guan (Xian Jiatong-Liverpool University, China), Ji Hua Ang (National University of Singapore, Singapore), Kay Chen Tan (National University of Singapore, Singapore)and Abdullah Al Mamun (National University of Singapore, Singapore)
Copyright: 2008
Pages: 12
Source title:
Encyclopedia of Healthcare Information Systems
Source Author(s)/Editor(s): Nilmini Wickramasinghe (Illinois Institute of Technology, USA)and Eliezer Geisler (Illinois Institute of Technology, USA)
DOI: 10.4018/978-1-59904-889-5.ch091
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Abstract
This chapter proposes a novel method of incremental interference-free neural network training (IIFNNT) for medical datasets, which takes into consideration the interference each attribute has on the others. A specially designed network is used to determine if two attributes interfere with each other, after which the attributes are partitioned using some partitioning algorithms. These algorithms make sure that attributes beneficial to each other are trained in the same batch, thus sharing the same subnetwork while interfering attributes are separated to reduce interference. There are several incremental neural networks available in literature (Guan & Li, 2001; Su, Guan & Yeo, 2001). The architecture of IIFNNT employed some incremental algorithm: the ILIA1 and ILIA2 (incremental learning with respect to new incoming attributes) (Guan & Li, 2001).
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