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Automated feature selection with a distinction sensitive learning vector quantizer
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DOI：10.1016/0925-2312(94)00071-9.png)
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An extended version of Kohonen's Learning Vector Quantization (LVQ) algorithm, called Distinction Sensitive Learning Vector Quantization (DSLVQ), is introduced which overcomes a major problem of LVQ, the dependency on proper pre-processing methods for scaling and feature selection. The algorithm employs a weighted distance function and adapts the metric with learning. Highest weights are assigned to components in the input vectors which are most informative for classification; non-informative components are discarded. The algorithm is applied to the analyses of multi-channel EEG data and compared with experienced methods.
Keyword:
learning vector quantization
distance function
automated feature selection
distinction sensitive learning vector quantization
EEG-data classification
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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