Unsupervised Feature Selection Using Iterative Shrinking and Expansion Algorithm

Tapas Bhadra

doi:10.1109/TETCI.2022.3199704

2022-12-01

PRE

Tapas Bhadra *

Ujjwal Maulik

DOI：10.1109/TETCI.2022.3199704

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摘要

En 中文

In this article, we propose an unsupervised feature selection algorithm based on the recently developed shrinking and expansion algorithm (SEA). The SEA is already an established, efficient algorithm to identify dense subgraphs within a weighted graph. To conduct SEA on a weighted graph, the dataset is first mapped onto an equivalent graph notation where each vertex corresponds to a feature, and the weight of each edge represents the normalized mutual information between the associated two features that form the edge. As a result, the feature selection problem is presented as the best way to obtain several dense sub-feature spaces by using the SEA on a weighted feature graph. The proposed feature selection algorithm consists of a two-stage structure, in which the first stage is utilized to find a number of dense feature subgraphs, whereas the second stage is used to identify a representative feature from each of these feature subgraphs. The main advantage of the proposed algorithm is that the users are not required to provide the number of features to be selected, which is a major flaw in most of the existing algorithms. The current findings from the researchers' method are better than the other state-of-the-art algorithms because the feature selection process identifies approximately one-fifth to one-third of the number of original features while providing a high accuracy score. The superiority of the proposed algorithm over the other conventional methods of feature selection is established for a number of real-life datasets. In addition, the performance of the proposed feature selection algorithm is also found to be better than other algorithms in one of the innovative application areas of computational intelligence such as ambient intelligence.

Keyword:

Feature extraction

Mutual information

Task analysis

Computational intelligence

Approximation algorithms

Probability distribution

Laplace equations

Pattern recognition

unsupervised feature selection

mutual information

normalized mutual information