LASSO: A feature selection technique in predictive modeling for machine learning

By M. R, R. R. 2016 IEEE International Conference on Advances in Computer Applications (ICACA), 2016

518 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7075280859097a9db26442a0ad7391d9511ae422

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tailin Liang
2. C. John Glossner
3. Shaobo Shi
4. Lei Wang
5. Elias Frantar
6. Dan Alistarh
7. Lingao Xiao
8. Yang He

In Computer Vision and Pattern Recognition

1. Tailin Liang
2. C. John Glossner
3. Shaobo Shi
4. Lei Wang
5. Elias Frantar
6. Dan Alistarh
7. Lingao Xiao
8. Yang He

In Statistics and Probability

1. Tailin Liang
2. C. John Glossner
3. Shaobo Shi
4. Lei Wang

Citations in the corpus

• Pruning and Quantization for Deep Neural Network Acceleration: A Survey (2021, methodology): “This operation is also referred to as LASSO feature selection and has been shown to perform better than traditional procedures such as OLS by selecting the most significantly contributed variables instead of using all the variables, which leads to approximately 60% more sparsity than OLS [171].”