RLVQ determination using OWA operators
نویسندگان
چکیده
Relevance Learning Vector Quantization (RLVQ) (introduced in [1]) is a variation of Learning Vector Quantization (LVQ) which allows a heuristic determination of relevance factors for the input dimensions. The method is based on Hebbian learning and defines weighting factors of the input dimensions which are automatically adapted to the specific problem. These relevance factors increase the overall performance of the LVQ algorithm. At the same time, relevances can be used for feature ranking and input dimensionality reduction. We introduce a different method for computing the relevance of the input dimensions in RLVQ. The relevances are computed on-line as Ordered Weighted Aggregation (OWA) weights. OWA operators are a family of mean type aggregation operators [2]. The principal benefit of our OWA-RLVQ algorithm is that it connects RLVQ to the mathematically consistent OWA models.
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* On leave of absence from the Department of Electronics and Computers, Transylvania University of Braşov. Abstract – Ordered Weighted Aggregation (OWA) operators represent a distinct family of aggregation operators and were introduced by Yager in [1]. They compute a weighted sum of a number of criteria that must be satisfied. The central element of the OWA operators is that the criteria are re...
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