<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>

CC BY-SA 4.0
2022-05-20T14:02:55.623Z
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
Neil Slater
CC BY-SA 4.0
2022-05-20T14:53:08.123Z
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.
NikoNyrh
CC BY-SA 4.0
2022-05-20T12:57:03.663Z
2022-05-20T12:57:03.663Z
NikoNyrh
<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>

CC BY-SA 4.0
2022-05-20T12:11:41.420Z
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".
nbro
<machine-learning><deep-learning><classification><unsupervised-learning>
I have a 3 class classification problem. Detection of one of classes is very important. How to design the problem? one class classification or ...?
Hassan Zohrevand
# 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".  

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## 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.  

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