HEMDAG (version 2.1.3)

TPR-DAG-variants: TPR-DAG Variants

Description

Function gathering several hierarchical ensemble algorithms

Usage

TPR.DAG(S, g, root = "00", positive = "children",
  bottomup = "threshold.free", t = 0, w = 0)

Arguments

S

a named flat scores matrix with examples on rows and classes on columns

g

a graph of class graphNEL. It represents the hierarchy of the classes

root

name of the class that it is on the top-level of the hierarchy (def. root="00")

positive

choice of the positive nodes to be considered in the bottom-up strategy. Can be one of the following values:

• children (def.): for each node are considered its positive children;

• descendants: for each node are considered its positive descendants;

bottomup

strategy to enhance the flat predictions by propagating the positive predictions from leaves to root. It can be one of the following values:

• threshold.free (def.): positive nodes are selected on the basis of the threshold.free strategy (def.);

• threshold: positive nodes are selected on the basis of the threshold strategy;

• weighted.threshold.free: positive nodes are selected on the basis of the weighted.threshold.free strategy;

• weighted.threshold: positive nodes are selected on the basis of the weighted.threshold strategy;

• tau: positive nodes are selected on the basis of the tau strategy. NOTE: tau is only a DESCENS variants. If you use tau strategy you must set the parameter positive=descendants;

t

threshold for the choice of positive nodes (def. t=0.5). Set t only for the variants that requiring a threshold for the selection of the positive nodes, otherwise set t to zero

w

weight to balance between the contribution of the node \(i\) and that of its positive nodes. Set w only for the weighted variants, otherwise set w to zero

Value

a named matrix with the scores of the classes corrected according to the chosen algorithm

See Also

TPR-DAG, DESCENS, HTD-DAG

Examples

# NOT RUN {
data(graph);
data(scores);
data(labels);
root <- root.node(g);
S.hier <- TPR.DAG(S, g, root, positive="children", bottomup="threshold.free", t=0, w=0);
# }