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Feature weighting for data analysis via evolutionary simulation

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abstract

We analyze an algorithm for assigning weights prior to scalarization in discrete multi-objective problems arising from data analysis. The algorithm evolves weights (interpreted as the relevance of features) by a replicator-type dynamic on the standard simplex, with update indices computed from a normalized data matrix. We prove that the resulting sequence converges globally to a unique interior equilibrium, yielding non-degenerate limiting weights.

fields

cs.NE 1

years

2026 1

verdicts

UNVERDICTED 1

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