.png){kind=icon}
Return
Ordered incremental training for GA-based classifiers
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2005.04.001.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes ordered incremental genetic algorithms (OIGAs) to address the incremental training of input attributes for GA-based classifiers. Rather than learning attributes in batch as with normal GAs, OIGAs learn attributes one after another. Attributes are first arranged in different orders by evaluating their individual discriminating ability. Classification rule sets are then evolved incrementally to accommodate attributes continuously. By experimenting with different attribute orders, different approaches of OIGAs are evaluated using benchmark classification data sets. The simulation results show that OIGAs can achieve generally better performance than normal GAs, and OIGAs perform the best when training with a descending order of attributes. (c) 2005 Elsevier B.V. All rights reserved.
Keywords:
genetic algorithms
incremental training
ordered incremental genetic algorithms
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
8.0K
Citations:
1.6W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSaveShareSaveShareSave
View more