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  • 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