hann (version 1.2)

combine: Combine Several Neural Nets for Prediction

Description

Combine several neural networks optimized with different sets of (overlapping) classes. The patterns (xi) are classified with all networks and the signal are combined to make a final prediction.

Usage

combine(nets, xi)

Value

a vector of predicted classes (as coded by the labels).

Arguments

nets

a list of objects inheriting class "hann".

xi

a matrix of patterns.

Details

Suppose we want to classify (predict) patterns labelled A, B, and C, then we can optimize two networks: one classifying A and B, and another one classifying B and C. The present function combines the predictions from both networks to classify patterns into the three classes.

Since optimizing a network with many classes can be complicated, it might be a better strategy to optimize separate networks with two classes each, and then combine them to make predictions over all classes.

See Also

hann, predict.hann1

Examples

## see ?hann for explanation on rpat():
rpat <- function(type, nr = 9L, nc = 9L,
                 signal = c(200, 255), noise = c(0, 50))
{
    ij <- switch(type,
                 "V" = cbind(1:nr, ceiling(nc/2)),
                 "H" = cbind(ceiling(nr/2), 1:nc),
                 "U" = cbind(nr:1, 1:nc),
                 "D" = cbind(1:nr, 1:nc))
    x <- matrix(runif(nr * nc, noise[1], noise[2]), nr, nc)
    x[ij] <- runif(nr, signal[1], signal[2])
    round(x)
}

## take 3 patterns:
labs <- c("V", "H", "U")
## repeat each label 40 times:
cl <- rep(labs, each = 40)
## simulate a pattern for each label:
xi <- t(sapply(cl, rpat))
## binarize the patterns:
xi <- binarize(xi)

## we take only 2 patterns in turn
## S: list with the indices for each pair
S <- list(1:80, c(1:40, 81:120), 41:120)
P <- list(c("V", "H"), c("V", "U"), c("H", "U"))

cl2 <- gl(2, 40) # only 2 classes for each net
## so the class (1 or 2) will be the same for each net
## but the labels will be different (from the list 'P')

ctr <- control.hann(quiet = TRUE)

NT <- vector("list", 3) # to store the optimized nets
for (i in 1:3) {
    s <- S[[i]]
    sig <- buildSigma(xi[s, ], quiet = TRUE)
    NT[[i]] <- hann3(xi[s, ], sig, cl2, H = 10,
                     labels = P[[i]], control = ctr)
}

## final predictions:
table(cl, combine(NT, xi))