ML Project - Operator Decision

1st Meeting

TODO:

• Read data
• Read report from student
• Reproduce the MLP model on our own
• Check the statistical model (methods used for the first classification) -> see paper
• Check other paper for methods for false alarm detection (See project proposal)

Additional notes:

• we use features calculated from data rather than fill data with 0 when we have different resolutions (better performance)
  • option for later: add own features
• database: only alarms -> classified as good/bad
• data unit: the full profile -> reject/accept the whole profile as bad/good
• use Pytorch
• Meeting One day/week -> 16h friday 12.04
• problem with database : not large
  • careful by splitting -> require class balance (reduces number of data used)
  • depending on splitting -> different performance
  • solutions: replicate database, class balance (but for now just implement it like this)
• historical data -> from boyes; what about the other datasets ?
• in-situ -> field study