resemble (version 3.0.1)

neighbors: Neighbor selection methods

Description

These functions create configuration objects that specify how neighbors are selected for memory-based learning in mbl an liblex.

Usage

neighbors_k(k)
neighbors_diss(threshold, k_min = 4L, k_max = Inf)

Value

An object of class c("neighbors_k", "neighbors") or c("neighbors_diss", "neighbors"), containing the validated parameters. Intended to be passed to mbl.

Arguments

k

Integer vector. One or more neighborhood sizes to evaluate. Values will be sorted in ascending order. Minimum allowed value is 4.

threshold

Numeric vector. One or more dissimilarity thresholds. Neighbors are selected if their dissimilarity to the target observation is below the threshold. Values will be sorted in ascending order.

k_min

Integer. Minimum number of neighbors to retain, regardless of threshold. Default 4L.

k_max

Integer or Inf. Maximum number of neighbors to retain, regardless of threshold. Default Inf (no upper bound other than the size of the reference set).

Details

Two strategies are available for neighbor selection:

Fixed-k selection (neighbors_k)

A fixed number of nearest neighbors is selected for each target observation. Multiple values of k can be provided to evaluate different neighborhood sizes.

Dissimilarity-threshold selection (neighbors_diss)

Neighbors are selected based on a dissimilarity threshold. All reference observations with dissimilarity below the threshold are included. The k_min and k_max arguments provide bounds to ensure a reasonable neighborhood size regardless of the threshold. Multiple thresholds can be provided to evaluate different settings.

See Also

mbl

Examples

# Fixed neighborhood sizes
neighbors_k(k = 50)
neighbors_k(k = c(40, 60, 80, 100, 120))

# Dissimilarity threshold with default bounds
neighbors_diss(threshold = 0.3)

# Dissimilarity threshold with custom bounds
neighbors_diss(threshold = c(0.1, 0.2, 0.3), k_min = 10, k_max = 150)