PredictorX1 t1_j47qgxr wrote on January 13, 2023 at 7:18 PM

Expert knowledge could be encoded as rules whose output is used as features for a machine learning system. These rules would accept data you already have, and produce new data as conclusions which would be fed as extra variables to a modeling algorithm.

Meddhouib10 t1_j4andjc wrote on January 14, 2023 at 9:50 AM

Have any paper in mind that speaks about this stuff ?

PredictorX1 t1_j4azldr wrote on January 14, 2023 at 12:30 PM

No, but the idea is pretty straightforward. Assuming that experts can provide domain knowledge that can be coded as conditions or rules (IF engine_temperature > 95 AND coolant_pressure < 12 THEN engine_status = "CRITICAL"), these can be used to generate 0/1 flags based on existing data to augment the training variables.

This can be made much more complex by using actual expert systems or fuzzy logic. There are entire sections of the technical library for those. For fuzzy logic, I would recommend:

"The Fuzzy Systems Handbook"

by Earl Cox

ISBN-13: 978-0121942700

Tigmib OP t1_j4bcr6q wrote on January 14, 2023 at 2:30 PM

Thanks for that suggestion! Yeah I had thoughts about this. The problem is that plant crop probably has not so binary solutions like a engine status... Maybe a very simple "rule" (e.g. a functions of water access and crop yield) could be added into the loss function. If this easy expert knowledge output a high probability that the plant died (and yield=0) all y_train could be set to 0 also.... However, crop growth relies on so many events that happens during growth, that it would mean to implement many many rules...