Cost-Sensitive Feature Selection of Data with Errors

TL;DR

This paper introduces a novel approach for cost-sensitive feature selection in data with measurement errors, incorporating test and misclassification costs, and proposes algorithms validated on UCI datasets.

Contribution

It develops a new data model and algorithms for cost-sensitive feature selection considering measurement errors and costs, advancing practical applications in data mining.

Findings

Backtracking pruning reduces computational operations.

Heuristic algorithm often finds near-optimal solutions.

Algorithms perform well on UCI datasets.

Abstract

In data mining applications, feature selection is an essential process since it reduces a model's complexity. The cost of obtaining the feature values must be taken into consideration in many domains. In this paper, we study the cost-sensitive feature selection problem on numerical data with measurement errors, test costs and misclassification costs. The major contributions of this paper are four-fold. First, a new data model is built to address test costs and misclassification costs as well as error boundaries. Second, a covering-based rough set with measurement errors is constructed. Given a confidence interval, the neighborhood is an ellipse in a two-dimension space, or an ellipsoidal in a three-dimension space, etc. Third, a new cost-sensitive feature selection problem is defined on this covering-based rough set. Fourth, both backtracking and heuristic algorithms are proposed to deal with this new problem. The algorithms are tested on six UCI (University of California - Irvine) data sets. Experimental results show that (1) the pruning techniques of the backtracking algorithm help reducing the number of operations significantly, and (2) the heuristic algorithm usually obtains optimal results. This study is a step toward realistic applications of cost-sensitive learning.