Minimal cost feature selection of data with normal distribution measurement errors

TL;DR

This paper introduces a novel approach for minimal cost feature selection on numerical data with normal distribution measurement errors, balancing test and misclassification costs using a backtracking algorithm.

Contribution

It develops a new data model with confidence interval neighborhoods and proposes an efficient backtracking algorithm with pruning for feature selection under cost constraints.

Findings

Pruning techniques significantly improve algorithm efficiency.

Algorithm performs well on datasets with nearly 1000 objects.

Neighborhoods based on confidence intervals better preserve data information.

Abstract

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.