Documentation Index
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dplbnDE supports four BN structure options, controlled by the structure argument available in all algorithm functions.
Structure options
Naive Bayes
TAN (Chow-Liu)
Hill-Climbing
The simplest structure: the class is the sole parent of every feature. No edges between features.result <- DEbest(
NP = 30, G = 50,
data = car,
class.name = names(car)[7],
structure = "nb"
)
Tree Augmented Naive Bayes: each feature has the class plus at most one other feature as parent, selected by the Chow-Liu maximum spanning tree algorithm.result <- DEbest(
NP = 30, G = 50,
data = car,
class.name = names(car)[7],
structure = "tan" # alias for "tancl"
)
Greedy structure search using bnclassify::tan_hc. Accepts additional arguments like k (cross-validation folds).result <- jade(
NP = 30, G = 50,
data = car,
class.name = names(car)[7],
structure = "hc",
k = 3 # passed to tan_hc
)
Comparing structures
Run the same DE variant with different structures and compare CLL:
library(dplbnDE)
data(car)
cn <- names(car)[7]
res_nb <- lshade(NP=20, G=30, data=car, class.name=cn, structure="nb", verbose=0)
res_tan <- lshade(NP=20, G=30, data=car, class.name=cn, structure="tan", verbose=0)
res_hc <- lshade(NP=20, G=30, data=car, class.name=cn, structure="hc", verbose=0, k=3)
cat("NB CLL:", res_nb$BestCLL, "\n")
cat("TAN CLL:", res_tan$BestCLL, "\n")
cat("HC CLL:", res_hc$BestCLL, "\n")
A higher (less negative) CLL indicates a better-fitting discriminative model. TAN and HC typically outperform NB when features have meaningful pairwise dependencies.
... arguments are forwarded to bnclassify::tan_cl or bnclassify::tan_hc. For example, the k parameter for hill-climbing sets the number of cross-validation folds:
result <- jade(NP=30, G=50, data=car, class.name="class",
structure="hc", k=5)