Public Use Cases in Industry & R&D
Our Use Cases present the results of XTRACTIS modeling and include complete Benchmarks against Neural Networks, Boosted Trees, Random Forests, and Logistic Regression.
These studies illustrate the ability of XTRACTIS to automatically induce knowledge in the form of predictive and intelligible mathematical relationships from real-world data (public data or authorized private data).
For each application, we show how the induced decision system uses its fuzzy rules to compute explained predictions for new situations, i.e. unknown to the learning data set.
The benchmarks of XTRACTIS, Logistic Regression, Random Forest, Boosted Tree, and Neural Network are carried out on Test and External Test datasets (all the unknown cases of the reference dataset, i.e., not used for training or validation).
In these graphs, the Intelligibility Score IS and the Performance Score PS are calculated from the public Use Cases (UC) in the Industry and R&D sectors and that include benchmarks of models.
The center of a bubble corresponds to the point of coordinates IS -on top- and PS -on bottom. A bubble on the top-right corner is the Holy Grail for critical AI-based decision systems: an AI Technique which produces predictive models with the highest Performance and the highest Intelligibility.
As Logistic Regression is inapplicable to Regression (UC#02 and UC#15) and unavailable for UC#01, graph#1 displays the scores calculated for 5 UCs, but only for 4 modeling techniques. Graph#2 displays the scores of the 5 modeling techniques, calculated only for the 2 UCs for which Logistic Regression is available.
All quantitative metrics are defined in the use cases documents.
XTRACTIS robots explore a 27-dimension decision space of possible supply chain configurations to find the one that maximizes at best Operating Income given requests on local sales objectives, local production constraints, and ecological requirements. Objectives and constraints are either fuzzy (flexible) or binary (rigid).
