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Abstract: A technique to visualize the trained self-organizing maps (SOM)
networks results is discussed, and an approach for feature selection
based on SOM networks combining with the visualization technique is
presented. In the approach, the responsibilities of every dimensional
feature in SOM networks competitive neurons weights to the input data
are computed, and then the feature sets being, sensitive to pattern
recognition, which have the main responsibilities, are selected
accordingly. The experimental data sets including the well-known IRIS
data and gearbox failure data are used to test the approach. It is
proved from the investigation that efficient feature sets are chosen
easily from raw feature sets, and then pattern recognition of input data
is realized.
Key words: Pattern recognition Feature selection Self-organizing
maps networks Visualization
CLC No: TP1.8
TH17
国家重大基础研究项目基金(2003CB716207)和国家自然科学基金(50375047和50205009)资助项目.
Received 20040401, received in revised form 20020040930 |