Rounding Methods for Discrete Linear Classification

By Yann Chevaleyre, Frédéric Koriche, Jean-Daniel Zucker. 2013

24 citations recorded by the source corpus.

Subfields: Optimization & Training, Statistical Learning Theory

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “[60] propose randomized rounding, where non-integers are rounded up or”
Learning Optimized Risk Scores (2016, background): “[28] Chevaleyre, Yann, Frederic Koriche, and Jean-Daniel Zucker.”
Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, methodology): “…agreement with a statement, (Keeney and Gregory 2005), and by linear classi ers in which the weights are allowed to take values on a discrete set, (Chevaleyre et al. 2013, Golea and Marchand 1993), we propose the Discrete Level Support Vector Machines (DILSVM) where the feature scores can only…”
Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “M-of-N rule tables were originally proposed as auxiliary models that could be extracted from neural nets (Towell and Shavlik, 1993) but were recently proposed to be trained as stand-alone discrete linear classification models by Chevaleyre et al. (2013).”
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, background): “Or’s of and’s models are also a special case of another form of interpretable model called M of-N rules [12,15,35,42,44], inparticular when M=1.”
Methods and Models for Interpretable Linear Classification (2014, methodology): “[26] and manages to capture complex relationships between variables through an interesting mechanism using rules (e.”
Optimized Risk Scores (2017, methodology): “Our algorithm may be used to solve other problems with non-convex penalties and constraints, such as `0-regularized risk minimization [27], or discrete linear classication problems that minimize a convex loss over a small integers [4, 7, 24, 35].”
A Bayesian Approach to Learning Scoring Systems (2015, unknown): “However, rounded solutions do not approximate well the optimal discrete models, 25 which means that the process of rounding could compromise the quality of the scoring system.”
The fused lasso penalty for learning interpretable medical scoring systems (2017, methodology): “A scheme to learn binary-weighted linear functions with a hinge loss was proposed by [2].”
A Provable Algorithm for Learning Interpretable Scoring Systems (2018, methodology): “, [4]): minimizing a convex loss function with discrete weights is NP-complete.”
Experimental analysis of new algorithms for learning ternary classifiers (2015, methodology): “In this section, we will present three rounding algorithms more elaborated than the initial one provided by [1] and called Round− k − weights:”