首页|AE‑MCDM: an autoencoder‑based multi‑criteria decision‑making approach for unsupervised feature selection
AE‑MCDM: an autoencoder‑based multi‑criteria decision‑making approach for unsupervised feature selection
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NETL
NSTL
Springer Nature
Feature selection is a fundamental technique for reducing the dimensionality of high-dimensional data by identifying the most relevant features while discarding redundant or irrelevant ones. In unsupervised settings, where labeled data are unavailable and labeling is costly, efective feature selection becomes even more challenging. This paper proposes AE-MCDM, a novel unsupervised feature selection method that integrates autoencoder-based feature extraction with multi- criteria decision-making (MCDM). The autoencoder captures high-level feature representations, and the connection weights between input features and hidden neurons refect feature importance. These weights are then processed using MCDM to rank and select the most informative features. Unlike conventional unsupervised feature selection methods, AE-MCDM leverages deep representation learning to enhance feature evaluation. To the best of our knowledge, this is the frst attempt to combine autoencoders with MCDM for feature selection. Extensive experiments on various datasets demonstrate that AE-MCDM outperforms existing methods in terms of clustering performance, measured by metrics such as accuracy, precision, recall, and normalized mutual information (NMI), while also achieving competitive computational efciency.
Unsupervised feature selectionAutoencoderMulti-criteria decision-makingHigh-dimensional data
NETL
NSTL
Springer Nature
