On the Relationship Between Feature Selection and Classification Accuracy

By Andreas Janecek, Wilfried N. Gansterer, Michael Demel, Gerhard F. Ecker. Fuzzy Systems and Data Mining, 2008

344 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c21817de5dc8164022a2f077a516a1164fb8f5be

How researchers see this paper

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

1. Kipp W. Johnson
2. Benjamin S. Glicksberg
3. Khader Shameer
4. Joel T. Dudley
5. Partho P. Sengupta
6. Samina Khalid
7. Tarek Amr Abdallah
8. Tehmina Khalil

Citations in the corpus

• Machine learning in cardiovascular medicine: are we there yet? (2018, background): “43 It is reasonable to assume that the factors driving readmission in the different review regions are different.”
• A survey of feature selection and feature extraction techniques in machine learning (2014, methodology): “High dimensional data is problematic for classification algorithms due to high computational cost and memory usage [4].”
• Survey on Feature Selection (2015, background): “When it comes to unsupervised learning algorithms, such as clustering, Janecek et al. (2008) highlighted that the problem with high dimensional data (more features) is that it makes the proximity measures between the records more uniform, hence metrics such as distance and density become harder to…”