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Objective function for multi-label classification
The 2019 Stack Overflow Developer Survey Results Are InHow to use binary relevance for multi-label text classification?Question Regarding Multi-label probability predictionsSingle vs Multiple deep learning networks for multi-label classification?Multi Label Classification on Data Columns in TablesClass weight degrades Multi Label Classification PerformanceConnection between cross entropy and likelihood for multi-class soft label classificationLarge Numpy.Array for Multi-label Image Classification (CelebA Dataset)Multi-label classification model in python?Cross Entropy vs Entropy (Decision Tree)
$begingroup$
The customary objective function for multi-label (e.g. M labels) classification is binary cross-entropy. The problem is, if we use binary cross-entropy, we are assuming that the output labels are independent of each other, turning the problem to M independent binary classification problems. Is there any suitable objective function that makes the output labels to be dependent on each other?
machine-learning
asked May 30 '18 at 18:16
pythinkerpythinker
6431211
$endgroup$
bumped to the homepage by Community♦ 4 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
The customary objective function for multi-label (e.g. M labels) classification is binary cross-entropy. The problem is, if we use binary cross-entropy, we are assuming that the output labels are independent of each other, turning the problem to M independent binary classification problems. Is there any suitable objective function that makes the output labels to be dependent on each other?
machine-learning
asked May 30 '18 at 18:16
pythinkerpythinker
6431211
$endgroup$
bumped to the homepage by Community♦ 4 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
I guess the main idea about the whole concept is that the random process arei.i.d.
$endgroup$
– Vaalizaadeh
May 30 '18 at 20:03
1
$begingroup$
That objective would be an interesting objective to try, I am not aware of any! At the same time, how would you think one can take advantage of? What is your domain (target) that you are sure these labels are dependent? Take CelebA multilabel dataset (mmlab.ie.cuhk.edu.hk/projects/CelebA.html), some labels could be considered dependent Female/Lipstick/etc., but there are others are not, in which one could benefit from being them to be independent.
$endgroup$
– Majid Mortazavi
Aug 12 '18 at 18:31
1
$begingroup$
@MajidMortazavi Thanks for time and consideration. The application is medical coding. The advantage is that different diseases have something in common. Thanks for introducing a new dataset to me.
$endgroup$
– pythinker
Aug 12 '18 at 18:45
$begingroup$
If it is a neural network then all the classification is done together till the last layer where they are separated by using binary cross entropy. My understanding is that in neural networks like for image recognition the recognition is connected on all previous layers except the last one. So for neural networks it is not a big deal that the last layer predicts separately every category.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:41
add a comment |
$begingroup$
The customary objective function for multi-label (e.g. M labels) classification is binary cross-entropy. The problem is, if we use binary cross-entropy, we are assuming that the output labels are independent of each other, turning the problem to M independent binary classification problems. Is there any suitable objective function that makes the output labels to be dependent on each other?
machine-learning
asked May 30 '18 at 18:16
pythinkerpythinker
6431211
$endgroup$
The customary objective function for multi-label (e.g. M labels) classification is binary cross-entropy. The problem is, if we use binary cross-entropy, we are assuming that the output labels are independent of each other, turning the problem to M independent binary classification problems. Is there any suitable objective function that makes the output labels to be dependent on each other?
machine-learning
machine-learning
asked May 30 '18 at 18:16
pythinkerpythinker
6431211
asked May 30 '18 at 18:16
pythinkerpythinker
6431211
asked May 30 '18 at 18:16
pythinkerpythinker
6431211
asked May 30 '18 at 18:16
pythinkerpythinker
6431211
asked May 30 '18 at 18:16
pythinkerpythinker
6431211
6431211
bumped to the homepage by Community♦ 4 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 4 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
I guess the main idea about the whole concept is that the random process arei.i.d.
$endgroup$
– Vaalizaadeh
May 30 '18 at 20:03
1
$begingroup$
That objective would be an interesting objective to try, I am not aware of any! At the same time, how would you think one can take advantage of? What is your domain (target) that you are sure these labels are dependent? Take CelebA multilabel dataset (mmlab.ie.cuhk.edu.hk/projects/CelebA.html), some labels could be considered dependent Female/Lipstick/etc., but there are others are not, in which one could benefit from being them to be independent.
$endgroup$
– Majid Mortazavi
Aug 12 '18 at 18:31
1
$begingroup$
@MajidMortazavi Thanks for time and consideration. The application is medical coding. The advantage is that different diseases have something in common. Thanks for introducing a new dataset to me.
$endgroup$
– pythinker
Aug 12 '18 at 18:45
$begingroup$
If it is a neural network then all the classification is done together till the last layer where they are separated by using binary cross entropy. My understanding is that in neural networks like for image recognition the recognition is connected on all previous layers except the last one. So for neural networks it is not a big deal that the last layer predicts separately every category.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:41
add a comment |
$begingroup$
I guess the main idea about the whole concept is that the random process arei.i.d.
$endgroup$
– Vaalizaadeh
May 30 '18 at 20:03
1
$begingroup$
That objective would be an interesting objective to try, I am not aware of any! At the same time, how would you think one can take advantage of? What is your domain (target) that you are sure these labels are dependent? Take CelebA multilabel dataset (mmlab.ie.cuhk.edu.hk/projects/CelebA.html), some labels could be considered dependent Female/Lipstick/etc., but there are others are not, in which one could benefit from being them to be independent.
$endgroup$
– Majid Mortazavi
Aug 12 '18 at 18:31
1
$begingroup$
@MajidMortazavi Thanks for time and consideration. The application is medical coding. The advantage is that different diseases have something in common. Thanks for introducing a new dataset to me.
$endgroup$
– pythinker
Aug 12 '18 at 18:45
$begingroup$
If it is a neural network then all the classification is done together till the last layer where they are separated by using binary cross entropy. My understanding is that in neural networks like for image recognition the recognition is connected on all previous layers except the last one. So for neural networks it is not a big deal that the last layer predicts separately every category.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:41
$begingroup$
I guess the main idea about the whole concept is that the random process are
i.i.d.$endgroup$
– Vaalizaadeh
May 30 '18 at 20:03
$begingroup$
I guess the main idea about the whole concept is that the random process are
i.i.d.$endgroup$
– Vaalizaadeh
May 30 '18 at 20:03
1
1
$begingroup$
That objective would be an interesting objective to try, I am not aware of any! At the same time, how would you think one can take advantage of? What is your domain (target) that you are sure these labels are dependent? Take CelebA multilabel dataset (mmlab.ie.cuhk.edu.hk/projects/CelebA.html), some labels could be considered dependent Female/Lipstick/etc., but there are others are not, in which one could benefit from being them to be independent.
$endgroup$
– Majid Mortazavi
Aug 12 '18 at 18:31
$begingroup$
That objective would be an interesting objective to try, I am not aware of any! At the same time, how would you think one can take advantage of? What is your domain (target) that you are sure these labels are dependent? Take CelebA multilabel dataset (mmlab.ie.cuhk.edu.hk/projects/CelebA.html), some labels could be considered dependent Female/Lipstick/etc., but there are others are not, in which one could benefit from being them to be independent.
$endgroup$
– Majid Mortazavi
Aug 12 '18 at 18:31
1
1
$begingroup$
@MajidMortazavi Thanks for time and consideration. The application is medical coding. The advantage is that different diseases have something in common. Thanks for introducing a new dataset to me.
$endgroup$
– pythinker
Aug 12 '18 at 18:45
$begingroup$
@MajidMortazavi Thanks for time and consideration. The application is medical coding. The advantage is that different diseases have something in common. Thanks for introducing a new dataset to me.
$endgroup$
– pythinker
Aug 12 '18 at 18:45
$begingroup$
If it is a neural network then all the classification is done together till the last layer where they are separated by using binary cross entropy. My understanding is that in neural networks like for image recognition the recognition is connected on all previous layers except the last one. So for neural networks it is not a big deal that the last layer predicts separately every category.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:41
$begingroup$
If it is a neural network then all the classification is done together till the last layer where they are separated by using binary cross entropy. My understanding is that in neural networks like for image recognition the recognition is connected on all previous layers except the last one. So for neural networks it is not a big deal that the last layer predicts separately every category.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:41
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
What you're looking for is called "cost-sensitive classification". Most methods however don't work with label similarities, but rather with relative penalties for different types of misclassifications.
answered Nov 10 '18 at 16:40
anymous.askeranymous.asker
60618
$endgroup$
add a comment |
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1 Answer
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$begingroup$
What you're looking for is called "cost-sensitive classification". Most methods however don't work with label similarities, but rather with relative penalties for different types of misclassifications.
answered Nov 10 '18 at 16:40
anymous.askeranymous.asker
60618
$endgroup$
add a comment |
$begingroup$
What you're looking for is called "cost-sensitive classification". Most methods however don't work with label similarities, but rather with relative penalties for different types of misclassifications.
answered Nov 10 '18 at 16:40
anymous.askeranymous.asker
60618
$endgroup$
add a comment |
$begingroup$
What you're looking for is called "cost-sensitive classification". Most methods however don't work with label similarities, but rather with relative penalties for different types of misclassifications.
answered Nov 10 '18 at 16:40
anymous.askeranymous.asker
60618
$endgroup$
What you're looking for is called "cost-sensitive classification". Most methods however don't work with label similarities, but rather with relative penalties for different types of misclassifications.
answered Nov 10 '18 at 16:40
anymous.askeranymous.asker
60618
answered Nov 10 '18 at 16:40
anymous.askeranymous.asker
60618
answered Nov 10 '18 at 16:40
anymous.askeranymous.asker
60618
answered Nov 10 '18 at 16:40
anymous.askeranymous.asker
60618
60618
add a comment |
add a comment |
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$begingroup$
I guess the main idea about the whole concept is that the random process are
i.i.d.$endgroup$
– Vaalizaadeh
May 30 '18 at 20:03
1
$begingroup$
That objective would be an interesting objective to try, I am not aware of any! At the same time, how would you think one can take advantage of? What is your domain (target) that you are sure these labels are dependent? Take CelebA multilabel dataset (mmlab.ie.cuhk.edu.hk/projects/CelebA.html), some labels could be considered dependent Female/Lipstick/etc., but there are others are not, in which one could benefit from being them to be independent.
$endgroup$
– Majid Mortazavi
Aug 12 '18 at 18:31
1
$begingroup$
@MajidMortazavi Thanks for time and consideration. The application is medical coding. The advantage is that different diseases have something in common. Thanks for introducing a new dataset to me.
$endgroup$
– pythinker
Aug 12 '18 at 18:45
$begingroup$
If it is a neural network then all the classification is done together till the last layer where they are separated by using binary cross entropy. My understanding is that in neural networks like for image recognition the recognition is connected on all previous layers except the last one. So for neural networks it is not a big deal that the last layer predicts separately every category.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:41