Abstract
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 [1], 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.
Original language | English |
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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 https://bnaic2021.uni.lu/ |
Conference
Conference | BNAIC/BENELEARN 2021 |
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Country/Territory | Luxembourg |
City | Esch-sur-Alzette |
Period | 10/11/21 → 12/11/21 |
Internet address |