Abstract
Abstract This article explores the utility of an efficient nearest neighbor (NN) search algorithm for applications in multisource kNN forest attribute imputation. The search algorithm reduces the number of distance calculations between a given target vector and each reference vector, thereby decreasing the time needed to discover the NN subset. Results of five trials show gains in NN search efficiency ranging from 75 to 98% for k = 1. The search algorithm can be easily incorporated into routines that optimize feature subsets or weights, values of k, distance decomposition coefficients, and mapping.
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