Linear feature sensibility for output partitioning in ordered neural incremental attribute learning

Ting Wang*, Sheng Uei Guan, Jieming Ma, Fangzhou Liu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Feature Ordering is a special training preprocessing for Incremental Attribute Learning (IAL), where features are trained one after another. Since most feature ordering calculation methods, compute feature ordering in one batch, no matter, this study presents a novel approach combining input feature ordered training and output partitioning for IAL to compute feature ordering with considering whether the output of the classification problem is univariate or multivariate. New metric called feature’s Single Sensibility (SS) is proposed to individually calculate features’ discrimination ability for each output. Finally, experimental benchmark results based on neural networks in IAL show that SS is applicable to calculates feature’s discrimination ability. Furthermore, combined output partitioning can also improve further the final classification performance effectively.

Original languageEnglish
Title of host publicationIntelligence Science and Big Data Engineering
Subtitle of host publicationBig Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers
EditorsZhi-Hua Zhou, Baochuan Fu, Fuyuan Hu, Zhancheng Zhang, Zhi-Yong Liu, Yanning Zhang, Xiaofei He, Xinbo Gao
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319238616
Publication statusPublished - 2015
Event5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 - Suzhou, China
Duration: 14 Jun 201516 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015


  • Fisher linear discriminant
  • Incremental attribute learning
  • Machine learning
  • Neural networks
  • Pattern classification

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