Instance-based regression methods generate solutions from prior solutions within a neighborhood of the input query. Their performance depends on both neighborhood selection criteria and on the method for generating new solutions from the values of prior instances. This paper proposes a new approach to addressing both problems, in which solutions are generated by an ensemble of solutions of local linear regression models built for a collection of "stretched" neighborhoods of the query. Each neighborhood is generated by relaxing a different dimension of the problem space. The rationale is to enable major change trends along that dimension to have increased influence on the corresponding model. The approach is evaluated for two candidate relaxation approaches, gradient-based and based on fixed profiles, and compared to baselines of k-NN and using a radius-based spherical neighborhood in n-dimensional space. Results in four test domains show up to 15 percent improvement over baselines, and suggest that the approach could be particularly useful in domains for which the space of prior instances is sparse.
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