Body: <p>i have raw measurement data of different events.</p>
  <p>My first approach was to calculate features of those events, do scaling, PCA and feature selection and then feed those features to different machine learning algorithms (ANN, RF, DT,...).  This works alright and up to now, to me this seems to be the only and correct way to go.</p>
  <p>Now i am wondering - are there machine learning approaches and techniques out there, which would let me feed the raw measurement data directly into the algorithm (so i can skip the whole feature engineering part)? One event is around 2000 measurement samples long.
  I could for example set up an ANN with 2000 input nodes and see where its going (just my naive first idea). Are there any hidden disadvantages to this - why is or isnt this a good idea?</p>
  <p>Up to now i couldnt find anything about raw measurement inputting - if you could post maybe just the names of the algorithms i should check out, it would be of great help.</p>
  <p>Thanks, regards and happy holidays</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-27T18:27:57.313Z
Comments.Text: I think you are rushing into feature engineering, you did not say what is the task at hand, classification, regression?
Comments.UserDisplayName: Dr. Snoopy
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-27T18:36:18.297Z
Comments.Text: The sensor supervises a manufacturing process, the tasks at hand are classification, for example whether there are anomalies in the workpiece or not, and regression, for example to estimate the sharpness of the tool.
Comments.UserDisplayName: toben aus
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-30T09:32:48.967Z
Comments.Text: I simply asked ChatGPT and bingo - Convolutional Neural Networks and Long Short Term Memories seem to be the correct answer. However, ChatGPT doesnt give me useful answers on the following: 1. I assume for very noisy measurement data, measurement data needs to be filtered and smoothed first?
  2. is there a third way of building a regression algorithm next to feature calculation as input and raw measurement data as input? Cheers
Comments.UserDisplayName: toben aus
Tags: <machine-learning>
Title: Machine Learning for raw measurement data
UserDisplayName: toben aus
ThreadText: # Machine Learning for raw measurement data
  
  (Asked by: username_0 on 2022-12-27)
  
  i have raw measurement data of different events.
  
  My first approach was to calculate features of those events, do scaling, PCA and feature selection and then feed those features to different machine learning algorithms (ANN, RF, DT,...). This works alright and up to now, to me this seems to be the only and correct way to go.
  
  Now i am wondering - are there machine learning approaches and techniques out there, which would let me feed the raw measurement data directly into the algorithm (so i can skip the whole feature engineering part)? One event is around 2000 measurement samples long.
  I could for example set up an ANN with 2000 input nodes and see where its going (just my naive first idea). Are there any hidden disadvantages to this - why is or isnt this a good idea?
  
  Up to now i couldnt find anything about raw measurement inputting - if you could post maybe just the names of the algorithms i should check out, it would be of great help.
  
  Thanks, regards and happy holidays
  
  **Tags:** machine-learning
  
  > **Comments:**
  > **username_1**: I think you are rushing into feature engineering, you did not say what is the task at hand, classification, regression?  
  > **username_0**: The sensor supervises a manufacturing process, the tasks at hand are classification, for example whether there are anomalies in the workpiece or not, and regression, for example to estimate the sharpness of the tool.  
  > **username_0**: I simply asked ChatGPT and bingo - Convolutional Neural Networks and Long Short Term Memories seem to be the correct answer. However, ChatGPT doesnt give me useful answers on the following: 1. I assume for very noisy measurement data, measurement data needs to be filtered and smoothed first? 2. is there a third way of building a regression algorithm next to feature calculation as input and raw measurement data as input? Cheers  
  
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  ## 0 answers
  
  