  Minimal cost feature selection is devoted to obtain a trade-off between test
costs and misclassification costs. This issue has been addressed recently on
nominal data. In this paper, we consider numerical data with measurement errors
and study minimal cost feature selection in this model. First, we build a data
model with normal distribution measurement errors. Second, the neighborhood of
each data item is constructed through the confidence interval. Comparing with
discretized intervals, neighborhoods are more reasonable to maintain the
information of data. Third, we define a new minimal total cost feature
selection problem through considering the trade-off between test costs and
misclassification costs. Fourth, we proposed a backtracking algorithm with
three effective pruning techniques to deal with this problem. The algorithm is
tested on four UCI data sets. Experimental results indicate that the pruning
techniques are effective, and the algorithm is efficient for data sets with
nearly one thousand objects.
