Feature selection, which identifies the most relevant and informative attributes of a dataset has been introduced to address the challenges raised by the emerge of high-dimensional data. Most existing feature selection methods are computationally inefficient; inefficient algorithms lead to high energy consumption, which is not desirable for devices with limited computational and energy resources. In , a novel and flexible method for unsupervised feature selection is proposed. This method, named "QuickSelection", introduces the strength of the neuron in sparse neural networks as a criterion to measure the feature importance. When tested on several benchmark datasets, the proposed method is able to achieve the best trade-off of classification and clustering accuracy, running time, and memory usage, among widely used approaches for feature selection.
|Title of host publication||BNAIC/BENELEARN 2021|
|Subtitle of host publication||The 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning|
|Number of pages||3|
|Publication status||Published - 2021|
|Event||BNAIC/BENELEARN 2021: The 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning - University of Luxembourg, Belval Campus: Faculty of Science, Technology and Medicine, Esch-sur-Alzette, Luxembourg|
Duration: 10 Nov 2021 → 12 Nov 2021
|Period||10/11/21 → 12/11/21|