Computational Statistics and Data Analysis a Similarity Measure to Assess the Stability of Classification Trees

By B. Briand, Gilles R. Ducharme, V. Parache, C. Mercat-Rommens

How researchers see this paper

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

1. Lingxiao Huang
2. Simon Caton
3. Nisheeth K. Vishnoi
4. Christian Haas
5. L. Elisa Celis
6. Ninareh Mehrabi

Citations in the corpus

• Stable and Fair Classification (2019, background): “Stability is a crucial consideration in classification (Bousquet & Elisseeff, 2002; Mukherjee et al., 2006; Briand et al., 2009; Fawzi et al., 2018), and has been investigated in several real-world applications, e.”
• Fairness in Machine Learning: A Survey (2020, background): “While a number of approaches exist to determine model stability [39, 45, 171, 184, 243, 262], it has rarely been applied to ML research beyond notions of model convergence and traditional performance measures with similar objectives to cross-validation.”