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In this paper, we formulate a new variation of Relief called Sigmoid Weighted ReliefF Star (SWRF*).
We developed a new spatially weighted variation of Relief called Sigmoid Weighted ReliefF Star (SWRF*), and applied it to synthetic SNP data.
This framework has several component terms; a distinct specification of each of these terms leads to the formulation of a distinct variation of Relief.
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As such, we focus our attention on non-iterative variations of Relief.
Several previously described variations of Relief differ essentially in the neighbor weighting function.
In contrast to previous variations of Relief, SWRF* utilizes a soft neighbor weighting kernel.
MoRF facilitates the formulation of new variations of Relief by allowing the specification of new functions for component terms.
First, we present the MoRF framework, which can specify different variations of Relief by using component functions.
We also present a framework called the Modular Relief Framework (MoRF) that can be used to develop novel variations of Relief.
Many variations of Relief have been developed over the past two decades and several of them have been applied to single nucleotide polymorphism (SNP) data.
Furthermore, we developed a framework called the Modular Relief Framework (MoRF) that can be used to develop novel variations of the Relief algorithm, and we used MoRF to develop the SWRF* algorithm.
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