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Feature selection that selects a subset of most salient features and removes irrelevant, redundant and noisy features is a process commonly employed in machine learning to solve the high dimensionality problem.
The first variant selects CHs such that every dependant node is within a distance h hops from the nearest CH.
The multiplexer block receives the two sets of input and selects the final sum based on the select input from the previous stage.