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Transform a multiclass dataset into a multi-label one

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0

$begingroup$

I have a dataset of feature/label pairs. My labels are probabilities of each feature vector to belong to the K classes. Here is an example for K = 3:

D1 = { (V0, [0.33,0.33,0.33]), (V1, [0.9,0.07,0.03]), (V2, [0.5,0.25,0.25])... }

The probabilities are normalized for a given data point. Yet the task is more a multilabel one, and it would make more sense to have independent Bernoulli distributions e.g.

D2 = { (V0, [0.9,0.9,0.9]), (V1, [0.99,0.0,0.0]), (V2, [0.9,0.2,0.5])... }

Is there a trick (smart heuristic) out there which would allow me to transform D1 into D2 based on the way the probability weights are distributed in D1?

machine-learning multiclass-classification multilabel-classification labels

share|improve this question

asked 10 hours ago

user3091275user3091275

1285

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Can you be more specific? How did you exactly got from (V0, [0.33,0.33,0.33]) to (V0, [0.9,0.9,0.9]) or from (V0, [0.9,0.9,0.9]) to (V1, [0.99,0.0,0.0])?
  $endgroup$
  – Antonio Jurić
  8 hours ago
  

  
  
  
  

add a comment | 

0

$begingroup$

I have a dataset of feature/label pairs. My labels are probabilities of each feature vector to belong to the K classes. Here is an example for K = 3:

D1 = { (V0, [0.33,0.33,0.33]), (V1, [0.9,0.07,0.03]), (V2, [0.5,0.25,0.25])... }

The probabilities are normalized for a given data point. Yet the task is more a multilabel one, and it would make more sense to have independent Bernoulli distributions e.g.

D2 = { (V0, [0.9,0.9,0.9]), (V1, [0.99,0.0,0.0]), (V2, [0.9,0.2,0.5])... }

Is there a trick (smart heuristic) out there which would allow me to transform D1 into D2 based on the way the probability weights are distributed in D1?

machine-learning multiclass-classification multilabel-classification labels

share|improve this question

asked 10 hours ago

user3091275user3091275

1285

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Can you be more specific? How did you exactly got from (V0, [0.33,0.33,0.33]) to (V0, [0.9,0.9,0.9]) or from (V0, [0.9,0.9,0.9]) to (V1, [0.99,0.0,0.0])?
  $endgroup$
  – Antonio Jurić
  8 hours ago
  

  
  
  
  

add a comment | 

$begingroup$

I have a dataset of feature/label pairs. My labels are probabilities of each feature vector to belong to the K classes. Here is an example for K = 3:

D1 = { (V0, [0.33,0.33,0.33]), (V1, [0.9,0.07,0.03]), (V2, [0.5,0.25,0.25])... }

The probabilities are normalized for a given data point. Yet the task is more a multilabel one, and it would make more sense to have independent Bernoulli distributions e.g.

D2 = { (V0, [0.9,0.9,0.9]), (V1, [0.99,0.0,0.0]), (V2, [0.9,0.2,0.5])... }

Is there a trick (smart heuristic) out there which would allow me to transform D1 into D2 based on the way the probability weights are distributed in D1?

machine-learning multiclass-classification multilabel-classification labels

share|improve this question

asked 10 hours ago

user3091275user3091275

1285

$endgroup$

share|improve this question

asked 10 hours ago

user3091275user3091275

1285

share|improve this question

asked 10 hours ago

user3091275user3091275

1285

asked 10 hours ago

user3091275user3091275

1285

asked 10 hours ago

user3091275user3091275

1285