Feature selection with test cost constraint

TL;DR

This paper introduces a feature selection method that considers test cost constraints, balancing informativeness and resource expenditure, using CSP-based algorithms and heuristics for medium-sized datasets.

Contribution

It formulates feature selection with test cost constraints as a CSP, proposes backtracking and heuristic algorithms, and redefines related problems in rough set theory for new research insights.

Findings

Heuristic algorithm often finds the optimal solution.

Backtracking is effective for medium-sized data.

New CSP-based definitions in rough sets offer research directions.

Abstract

Feature selection is an important preprocessing step in machine learning and data mining. In real-world applications, costs, including money, time and other resources, are required to acquire the features. In some cases, there is a test cost constraint due to limited resources. We shall deliberately select an informative and cheap feature subset for classification. This paper proposes the feature selection with test cost constraint problem for this issue. The new problem has a simple form while described as a constraint satisfaction problem (CSP). Backtracking is a general algorithm for CSP, and it is efficient in solving the new problem on medium-sized data. As the backtracking algorithm is not scalable to large datasets, a heuristic algorithm is also developed. Experimental results show that the heuristic algorithm can find the optimal solution in most cases. We also redefine some existing feature selection problems in rough sets, especially in decision-theoretic rough sets, from the viewpoint of CSP. These new definitions provide insight to some new research directions.