A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure

TL;DR

This paper introduces a novel semidefinite programming-based search strategy for feature selection using mutual information, providing a polynomial-time approximation method that outperforms traditional heuristics.

Contribution

It proposes a new parallel search algorithm based on SDP for feature selection, linking it to the maximum-cut problem and deriving its approximation ratio.

Findings

The SDP-based method efficiently searches the feature subset space in polynomial time.

Most heuristic criteria are shown to be truncated versions of mutual information expansions.

The proposed algorithm's approximation ratio is comparable to established methods.

Abstract

Feature subset selection, as a special case of the general subset selection problem, has been the topic of a considerable number of studies due to the growing importance of data-mining applications. In the feature subset selection problem there are two main issues that need to be addressed: (i) Finding an appropriate measure function than can be fairly fast and robustly computed for high-dimensional data. (ii) A search strategy to optimize the measure over the subset space in a reasonable amount of time. In this article mutual information between features and class labels is considered to be the measure function. Two series expansions for mutual information are proposed, and it is shown that most heuristic criteria suggested in the literature are truncated approximations of these expansions. It is well-known that searching the whole subset space is an NP-hard problem. Here, instead of the conventional sequential search algorithms, we suggest a parallel search strategy based on semidefinite programming (SDP) that can search through the subset space in polynomial time. By exploiting the similarities between the proposed algorithm and an instance of the maximum-cut problem in graph theory, the approximation ratio of this algorithm is derived and is compared with the approximation ratio of the backward elimination method. The experiments show that it can be misleading to judge the quality of a measure solely based on the classification accuracy, without taking the effect of the non-optimum search strategy into account.