A hybrid FAM-CART model for online data classification

Manjeevan Seera, Chee Peng Lim, Shing Chiang Tan

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)


In this paper, an online soft computing model based on an integration between the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) for undertaking data classification problems is presented. Online FAM network is useful for conducting incremental learning with data samples, whereas the CART model prevails in depicting the knowledge learned explicitly in a tree structure. Capitalizing on their respective advantages, the hybrid FAM-CART model is capable of learning incrementally while explaining its predictions with knowledge elicited from data samples. To evaluate the usefulness of FAM-CART, 2 sets of benchmark experiments with a total of 12 problems are used in both offline and online learning modes. The results are examined and compared with those published in the literature. The experimental outcome positively indicates that the online FAM-CART model is useful for tackling data classification tasks. In addition, a decision tree is produced to allow users in understanding the predictions, which is an important property of the hybrid FAM-CART model in supporting decision-making tasks.

Original languageEnglish
Pages (from-to)562-581
Number of pages20
JournalComputational Intelligence
Issue number2
Publication statusPublished - May 2018
Externally publishedYes


  • classification and regression tree
  • data classification
  • fuzzy ARTMAP
  • online learning
  • rule extraction

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