PhD Student in Mathematics, Logic & Computer Science @ University of Ferrara & Parma, Italy.
Developing, studying and testing new symbolic learning methods.
Checkout ModalDecisionTrees.jl at: https://github.com/giopaglia/ModalDecisionTrees.jl
#AI, #Interpretability, #ModalDecisionTrees!
ModalDecisionTrees.jl offers a set of symbolic machine learning algorithms that extend classical decision tree learning algorithms, and are able to natively handle time series and image data. Modal Decision Trees leverage modal logics to perform a primitive-but-powerful form of entity-relation reasoning; this allows them to capture temporal and spatial patterns, and makes them suitable to natively deal (= no need for feature extraction) with data such as multivariate time-series and images.