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Using significant, positively associated and relatively class correlated rules for associative class...

Using significant, positively associated and relatively...

Using significant, positively associated and relatively class correlated rules for associative class...

https://devfeature-collection.sl.nsw.gov.au/record/74VMQQj23VQd

Using significant, positively associated and relatively class correlated rules for associative classification of imbalanced datasets / Florian Verhein and Sanjay Chawla.

About this item

Full title

Using significant, positively associated and relatively class correlated rules for associative classification of imbalanced datasets / Florian Verhein and Sanjay Chawla.

Author / Creator

Publisher

Sydney : School of Information Technologies, University of Sydney, 2007.

Date

2007.

Call Numbers

NQ004.0994/2

Record Identifier

74VMQQj23VQd

MMS ID

991000572369702626

Language

English

Formats

Physical Description

Physical content

[11] p. : ill. ; 30 cm.

Publication information

Publisher

Sydney : School of Information Technologies, University of Sydney, 2007.

Place of Publication

New South Wales

Date Published

2007.

More information

Alternative Titles

Full title

Using significant, positively associated and relatively class correlated rules for associative classification of imbalanced datasets / Florian Verhein and Sanjay Chawla.

Authors, Artists and Contributors

Author / Creator

Notes

General note

Technical report / University of Sydney. School of Information Technologies ; no. 614.

Cover title.

"August 2007".

Bibliography: p. [11]

Additional physical form availability note

Also available in an electronic version on the Internet. Address as of 25/7/08: http://www.cs.usyd.edu.au/f̃verhein/publications/verhein07sparccc_tr614.pdf.

Contextual Information

Other version (online)

Identifiers

Primary Identifiers

Call Numbers

NQ004.0994/2

Record Identifier

74VMQQj23VQd

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/74VMQQj23VQd

Other Identifiers

ISBN

9781864879230

DDC

658.4032

MMS ID

991000572369702626

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