13 citations · 28 across the 6 of their papers we have counts for
5 papers · 1 filter
Inverse classification with logistic and softmax classifiers: efficient optimization
Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada
In recent years, a certain type of problems have become of interest where one wants to query a trained classifier. Specifically, one wants to find the closest instance to a given i…
Very fast, approximate counterfactual explanations for decision forests
Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada
We consider finding a counterfactual explanation for a classification or regression forest, such as a random forest. This requires solving an optimization problem to find the close…
Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features
Suryabhan Singh Hada, Miguel Á. Carreira-Perpiñán, Arman Zharmagambetov
The widespread deployment of deep nets in practical applications has lead to a growing desire to understand how and why such black-box methods perform prediction. Much work has foc…
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms
Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada
We consider counterfactual explanations, the problem of minimally adjusting features in a source input instance so that it is classified as a target class under a given classifier.…
An Experimental Comparison of Old and New Decision Tree Algorithms
Arman Zharmagambetov, Suryabhan Singh Hada, Miguel Á. Carreira-Perpiñán +1
This paper presents a detailed comparison of a recently proposed algorithm for optimizing decision trees, tree alternating optimization (TAO), with other popular, established algor…