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

doi:10.1109/TKDE.2006.103

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

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	941-953
Journal	IEEE Transactions on Knowledge and Data Engineering
Volume	18
Issue number	7
DOIs	https://doi.org/10.1109/TKDE.2006.103
Publication status	Published - 1 Jul 2006

Bibliographical 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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10.1109/TKDE.2006.103

Cite this

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

author = "T McGinnity and DA Bell and G Prasad and Q Wu",

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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