Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing

By V. Sugumaran, V. Muralidharan, K. Ramachandran. 2007

480 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/2ec0ec3a6348b5137f5b7b64cdf45903e0970311

How researchers see this paper

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

In Control and Systems Engineering

1. Sina Tabakhi
2. Parham Moradi
3. Fardin Akhlaghian Tab

In Mechanical Engineering

1. Sina Tabakhi
2. Parham Moradi
3. Fardin Akhlaghian Tab

Citations in the corpus

• Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 (2020, methodology): “DT can be used for feature selection [57].”
• An unsupervised feature selection algorithm based on ant colony optimization (2014, methodology): “Consequently, in the DT-based embedded methods, first of all, the tree will be constructed using a collection of patterns, and then the features which are involved in the classification are selected as a final feature subset (Sugumaran et al., 2007; Tahir et al., 2006).”