Positive and negative fuzzy rule system, extreme learning machine and image classification

Wu Jun, Wang Shitong, Fu Lai Korris Chung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

104 Citations (Scopus)


We often use the positive fuzzy rules only for image classification in traditional image classification systems, ignoring the useful negative classification information. Thanh Minh Nguyen and QMJonathan Wu introduced the negative fuzzy rules into the image classification, and proposed combination of positive and negative fuzzy rules to form the positive and negative fuzzy rule system, and then applied it to remote sensing image/natural image classification. Their experiments demonstrated that their proposed method has achieved promising results. However, since their method was realized using the feedforward neural network model which requires adjusting the weights in the gradient descent way, the training speed is very slow. Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFNs) learning algorithm, which has distinctive advantages such as quick learning, good generalization performance. In this paper, the equivalence between ELM and the positive and negative fuzzy rule system is revealed, so ELM can be naturally used for training the positive and negative fuzzy rule system quickly for image classification. Our experimental results indicate this claim.
Original languageEnglish
Pages (from-to)261-271
Number of pages11
JournalInternational Journal of Machine Learning and Cybernetics
Issue number4
Publication statusPublished - 1 Dec 2011


  • Extreme learning machine
  • Fuzzy systems
  • Image classification
  • Positive and negative fuzzy rules

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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