A Self-Organizing Computing Network for Decision-Making in Data Sets with a Diversity of Data Types

T McGinnity, DA Bell, G Prasad, Q Wu

Research output: Contribution to journal › Article

4 Citations (Scopus)
LanguageEnglish
Pages941-953
JournalIEEE Transactions on Knowledge and Data Engineering
Volume18
Issue number7
DOIs
Publication statusPublished - 1 Jul 2006

Cite this

McGinnity, T ; Bell, DA ; Prasad, G ; Wu, Q. / A Self-Organizing Computing Network for Decision-Making in Data Sets with a Diversity of Data Types. In: IEEE Transactions on Knowledge and Data Engineering. 2006 ; Vol. 18, No. 7. pp. 941-953.
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note = "Other Details ------------------------------------ The importance of this paper lies in the fact that it addresses the problem of real world constraints on intelligent systems techniques. In the real world, an intelligent system often encounters mixed data types and incomplete or imprecise information, and these deficiencies pose difficult challenges. This paper considerably advances current approaches by proposing a new self-organising computing network to address real world problems that exhibit a combination of data types and by proving that such an approach is effective. An important aspect of the paper is that it integrates successfully traditional artificial intelligence approaches with methods based on computational intelligence.",
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McGinnity, T, Bell, DA, Prasad, G & Wu, Q 2006, 'A Self-Organizing Computing Network for Decision-Making in Data Sets with a Diversity of Data Types', IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 7, pp. 941-953. https://doi.org/10.1109/TKDE.2006.103

A Self-Organizing Computing Network for Decision-Making in Data Sets with a Diversity of Data Types. / McGinnity, T; Bell, DA; Prasad, G; Wu, Q.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 7, 01.07.2006, p. 941-953.

Research output: Contribution to journal › Article

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AU - Prasad, G

AU - Wu, Q

N1 - Other Details ------------------------------------ The importance of this paper lies in the fact that it addresses the problem of real world constraints on intelligent systems techniques. In the real world, an intelligent system often encounters mixed data types and incomplete or imprecise information, and these deficiencies pose difficult challenges. This paper considerably advances current approaches by proposing a new self-organising computing network to address real world problems that exhibit a combination of data types and by proving that such an approach is effective. An important aspect of the paper is that it integrates successfully traditional artificial intelligence approaches with methods based on computational intelligence.

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McGinnity T, Bell DA, Prasad G, Wu Q. A Self-Organizing Computing Network for Decision-Making in Data Sets with a Diversity of Data Types. IEEE Transactions on Knowledge and Data Engineering. 2006 Jul 1;18(7):941-953. https://doi.org/10.1109/TKDE.2006.103

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