Interpretable Concept-Based Classification with Shapley Values

Among the family of rule-based classification models, there
are classifiers based on conjunctions of binary attributes. For example,
JSM-method of automatic reasoning (named after John Stuart Mill) was
formulated as a classification technique in terms of intents of formal concepts as classification hypotheses. These JSM-hypotheses already represent interpretable model since the respective conjunctions of attributes
can be easily read by decision makers and thus provide plausible reasons
for model prediction. However, from the interpretable machine learning
viewpoint, it is advisable to provide decision makers with importance
(or contribution) of individual attributes to classification of a particular
object, which may facilitate explanations by experts in various domains
with high-cost errors like medicine or finance. To this end, we use the
notion of Shapley value from cooperative game theory, also popular in
machine learning. We provide the reader with theoretical results, basic
examples and attribution of JSM-hypotheses by means of Shapley value
on real data.