The Inverse Classification Problem
By Charu C. Aggarwal, Cheng Chen, Jiawei Han. 2010
47 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
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
- Salvatore Ruggieri
- Isabel Valera
- Dino Pedreschi
- Riccardo Guidotti
- Bernhard Schölkopf
- Fosca Giannotti
- Anna Monreale
- Franco Turini
In Signal Processing
- Fosca Giannotti
- Isabel Valera
- Gilles Barthe
- Amir-Hossein Karimi
- Dino Pedreschi
- Riccardo Guidotti
- Franco Turini
- Bernhard Schölkopf
Citations in the corpus
- Local Rule-Based Explanations of Black Box Decision Systems (2018, methodology): “3If instead of a black box, we are given a machine learning model, this problem is known as the inverse classification problem [1].”
- Actionable Recourse in Linear Classification (2018, background): “Our work can be viewed as an application of inverse classification [1], which aims to determine how the inputs to a prediction model can be manipulated to obtain a desired outcome [see e.”
- A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “For instance, cost-minimizing inverse classification problem [4, 101–103, 105, 118], aim to identify the “minimum required change to a case in order to reclassify it as a member of a different preferred class?””
- Counterfactual Explanations for Machine Learning: A Review (2020, background): “• Inverse classification - Inverse classification aims to perturb an input in a meaningful way in order to classify it into its desired class [12, 72].”
- Generalized Inverse Classification (2016, background): “In [3, 8] there are no constraints imposed.”
- Explaining machine learning models in sales predictions (2017, unknown): “In a related sensitivity based approach, called inverse classification, Aggarwal et al. (2010) try to determine the minimum required change to a data point in order to reclassify it as a member of a different class.”
- Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review (2020, unknown): “• Inverse classification: Inverse classification aims to perturb an input in a meaningful way in order to classify it into its desired class [4, 189].”