Abstract
When employing a similarity function to measure the similarity between two cases, one large problem is how to determine the feature weights. This paper presents a new method for learning feature weights in a similarity function from the given similarity information. The similarity information can be divided into two kinds: One is called qualitative similarity information which represents the similarities between cases. The other is called relative similarity information which represents the relation between similarities of two case pairs both including a same case. We apply genetic algorithms to learn feature weights from these two kinds of information respectively. The proposed genetic algorithms are applicable to both linear and nonlinear similarty functions. Our experiments show the learning results are better even if the given similarity information include errors.
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