Incremental ordered neural network training

Sheng Uei Guan*, Jun Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


This paper investigates the incremental training of a Neural Network (NN) with the input attributes introduced in order. A specially designed NN is used to evaluate the individual discrimination ability of each input attribute. Attributes are then sorted in descending, ascending, and random orders of their individual discrimination abilities and introduced into another NN being trained with an incremental training algorithm, ITID. To reduce the interference caused by irrelevant features and high-complexity tasks, only relevant features are involved and tasks are decomposed in the experiments. The experimental results of several benchmark problems show that descending order obtains the highest generalization accuracy among the three training orders for both classification and regression problems.

Original languageEnglish
Pages (from-to)137-172
Number of pages36
JournalJournal of Intelligent Systems
Issue number3
Publication statusPublished - 2002
Externally publishedYes


  • Incremental training
  • Input attributes
  • Neural networks
  • Ordered training

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