An Efficient Explanation of Individual Classifications using Game Theory

@article{trumbelj2010AnEE,
  title={An Efficient Explanation of Individual Classifications using Game Theory},
  author={Erik {\vS}trumbelj and Igor Kononenko},
  journal={J. Mach. Learn. Res.},
  year={2010},
  volume={11},
  pages={1-18}
}
We present a general method for explaining individual predictions of classification models. The method is based on fundamental concepts from coalitional game theory and predictions are explained with contributions of individual feature values. We overcome the method's initial exponential time complexity with a sampling-based approximation. In the experimental part of the paper we use the developed method on models generated by several well-known machine learning algorithms on both synthetic and… 
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References

SHOWING 1-10 OF 30 REFERENCES

Feature Selection via Coalitional Game Theory

Empirical comparison with several other existing feature selection methods shows that the backward elimination variant of CSA leads to the most accurate classification results on an array of data sets.

Explaining Classifications For Individual Instances

It is demonstrated that the generated explanations closely follow the learned models and a visualization technique is presented that shows the utility of the approach and enables the comparison of different prediction methods.

Polynomial calculation of the Shapley value based on sampling

Explaining instance classifications with interactions of subsets of feature values

Fair Attribution of Functional Contribution in Artificial and Biological Networks

The multi-perturbation Shapley value analysis, an axiomatic, scalable, and rigorous method for deducing causal function localization from multiple perturbations data, accurately quantifies the contributions of network elements and their interactions.

Visual Explanation of Evidence with Additive Classifiers

A framework, ExplainD, is described for explaining decisions made by classifiers that use additive evidence, which applies to many widely used classifiers, including linear discriminants and many additive models.

Contact personalization using a score understanding method

This paper presents a method to interpret the output of a classification (or regression) model based on two concepts: the variable importance and the value importance of the variable.

Wrappers for Feature Subset Selection

Random Forests

Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.

Transversality of the Shapley value

The paper by S. Moretti and F. Patrone is a remarkable survey on the use of the Shapley value in many different domains (so different that one could also entitle the paper “Versatility of the Shapley…