Explanations for Monotonic Classifiers
In many classification tasks there is a requirement of monotonicity. Concretely, if all else remains constant, increasing (resp. decreasing) the value of one or more features must not decrease (resp. increase) the value of the prediction. Despite comprehensive efforts on learning monotonic classifiers, dedicated approaches for explaining monotonic classifiers are scarce and classifier-specific. This paper describes novel algorithms for the computation of one formal explanation of a (black-box) monotonic classifier. These novel algorithms are polynomial in the run time complexity of the classifier and the number of features. Furthermore, the paper presents a practically efficient model-agnostic algorithm for enumerating formal explanations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Sufficient, Necessary and Complete Causal Explanations in Image Classification
Existing algorithms for explaining the outputs of image classifiers are based on a variety of approaches and produce explanations that frequently lack formal rigour. On the other hand, logic-based explanations are formal…
Image ClassificationCan I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models
In recent years, explainable machine learning methods have been very successful. Despite their success, most explainable machine learning methods are applied to black-box models without any domain knowledge. By incorpora…
On Interactive Explanations as Non-Monotonic Reasoning
Recent work shows issues of consistency with explanations, with methods generating local explanations that seem reasonable instance-wise, but that are inconsistent across instances. This suggests not only that instance-w…
SpecificityLabel Noise Filtering Techniques to Improve Monotonic Classification
The monotonic ordinal classification has increased the interest of researchers and practitioners within machine learning community in the last years. In real applications, the problems with monotonicity constraints are v…
ClassificationGeneral ClassificationOrdinal ClassificationOn the Within-Group Fairness of Screening Classifiers
Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes. In this context, it has been recently shown that, if a classifier is calibrated, one can identify the smal…
Fairness