Output details
11 - Computer Science and Informatics
University of Surrey
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Output 0 of 0 in the submission
Output title
Hybrid ACO and TOFA feature selection approach for text classification
Type
E - Conference contribution
Name of conference/published proceedings
IEEE Congress on Evolutionary Computation (CEC)
Volume number
-
Issue number
-
First page of article
1
ISSN of proceedings
-
Year of publication
2012
URL
-
Number of additional authors
-
Additional information
<24>The novelty of this paper is the introduction of a new technique to combine two optimisation methods, ACO and TOFA, for automatic feature selection for classification. This results in more accurate classification than each method individually. The method has demonstrated its success in text classification and is also applicable to images. We are currently extending it to medical image analysis: when clinical signs vary from patient to patient, the difference between initial presence of a clinical sign and a normal case is very subtle, whilst the classification task itself requires high precision, and is improved by this method.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
