Answers.Body: <p>When one class is more important than the others, you can simply use a higher sample weight for those and use normal three-class predictor.</p>
  <p>Alternatively you can bias the network to a desired direction. Let's say the detection of class B is more important, and the model predicts class probabilities of 50%, 40% and 20% for classes A, B and C. You could &quot;override&quot; the prediction to B when ever its probability is above a certain threshold, for example 35%. Naturally this will increase the odds for false positives.</p>
  <p>Maybe this threshold tuning is easier if you use &quot;B vs. the rest&quot; classifier first, you'll get clear false-positive vs. false-negative plots for given thresholds. The secondary classifier would be trained for an &quot;A vs C&quot; problem.</p>
  
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Answers.CreationDate: 2022-05-20T14:02:55.623Z
Answers.Text: May be worth linking some examples of sample weight in use. In some libraries it may be called class weight (it can be viewed as "weighting a sample due to its class" or "weighting all samples of given class") so the name varies
Answers.UserDisplayName: Neil Slater
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Answers.CreationDate: 2022-05-20T14:53:08.123Z
Answers.Text: Maybe, but I don't know which library the OP is using. I expect it is trivial to set the weights, and if not he may ask an other question.
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Answers.UserDisplayName: NikoNyrh
Body: <p>I have a 3 class classification problem. Correct detection of one of the classes is very important. How to design the problem:</p>
  <ol>
  <li>one class classification?</li>
  <li>a normal 3 class classification?</li>
  <li>two distinct 2 class classification?(I mean, for example I have 3 lables A,B,C , and detection of class B is very important. Design a binary classification A,B and another binary classification B,C ?</li>
  <li>or ....?
  Please recommend me!</li>
  </ol>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-05-20T12:11:41.420Z
Comments.Text: What have you found and tried so far? Share with us that info. Also, what do you mean by "design a problem"? Maybe you mean "design an algorithm or model to solve this problem" or "describe the problem mathematically".
Comments.UserDisplayName: nbro
Tags: <machine-learning><deep-learning><classification><unsupervised-learning>
Title: I have a 3 class classification problem. Detection of one of classes is very important. How to design the problem? one class classification or ...?
UserDisplayName: Hassan Zohrevand
ThreadText: # I have a 3 class classification problem. Detection of one of classes is very important. How to design the problem? one class classification or ...?
  
  (Asked by: username_0 on 2022-05-20)
  
  I have a 3 class classification problem. Correct detection of one of the classes is very important. How to design the problem:
  
  1. one class classification?
  2. a normal 3 class classification?
  3. two distinct 2 class classification?(I mean, for example I have 3 lables A,B,C , and detection of class B is very important. Design a binary classification A,B and another binary classification B,C ?
  4. or ....?
     Please recommend me!
  
  **Tags:** machine-learning, deep-learning, classification, unsupervised-learning
  > **Comments:**
  > **username_1**: What have you found and tried so far? Share with us that info. Also, what do you mean by "design a problem"? Maybe you mean "design an algorithm or model to solve this problem" or "describe the problem mathematically".  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2022-05-20. Score: 1
  
  When one class is more important than the others, you can simply use a higher sample weight for those and use normal three-class predictor.
  
  Alternatively you can bias the network to a desired direction. Let's say the detection of class B is more important, and the model predicts class probabilities of 50%, 40% and 20% for classes A, B and C. You could "override" the prediction to B when ever its probability is above a certain threshold, for example 35%. Naturally this will increase the odds for false positives.
  
  Maybe this threshold tuning is easier if you use "B vs. the rest" classifier first, you'll get clear false-positive vs. false-negative plots for given thresholds. The secondary classifier would be trained for an "A vs C" problem.
  
  > **Comments:**
  > **username_3**: May be worth linking some examples of sample weight in use. In some libraries it may be called class weight (it can be viewed as "weighting a sample due to its class" or "weighting all samples of given class") so the name varies  
  > **username_2**: Maybe, but I don't know which library the OP is using. I expect it is trivial to set the weights, and if not he may ask an other question.  
  
  ---
  
  