Is this approach for Active Learning correct?
$begingroup$
Let's say that we have 10,000 unlabeled documents, and we want to use pool-based sampling with batch size of 5. And we will use least confidence (LC) for querying strategy.
Is this outline of active learning reasonable:
1) Randomly select 30 documents, and get them labelled:=> labelled: 30, unlabelled: 9970.
2) Train the model on 30 labelled data
3) Use the model to make predictions for 9970 unlabelled documents
4) Use LC to select 5 out of all predicted documents that the model is least certain about
5) Get labels for the 5 documents:=> labelled: 35, unlabelled: 9965
6) Retrain the model on labelled data
7) Keep doing steps 2 - 6 until, the labeling resource expires or other stopping criteria is met.
Is this correct? I am mostly concerned about step 3) Should we retrain on all the data on every iteration? It seems like it could get computationally slow, but on the other hand if we don't evaluate every document how would we know which one is the most helpful to get labelled?
machine-learning active-learning
asked yesterday
AkavallAkavall
29017
$endgroup$
add a comment |
$begingroup$
Let's say that we have 10,000 unlabeled documents, and we want to use pool-based sampling with batch size of 5. And we will use least confidence (LC) for querying strategy.
Is this outline of active learning reasonable:
1) Randomly select 30 documents, and get them labelled:=> labelled: 30, unlabelled: 9970.
2) Train the model on 30 labelled data
3) Use the model to make predictions for 9970 unlabelled documents
4) Use LC to select 5 out of all predicted documents that the model is least certain about
5) Get labels for the 5 documents:=> labelled: 35, unlabelled: 9965
6) Retrain the model on labelled data
7) Keep doing steps 2 - 6 until, the labeling resource expires or other stopping criteria is met.
Is this correct? I am mostly concerned about step 3) Should we retrain on all the data on every iteration? It seems like it could get computationally slow, but on the other hand if we don't evaluate every document how would we know which one is the most helpful to get labelled?
machine-learning active-learning
asked yesterday
AkavallAkavall
29017
$endgroup$
add a comment |
$begingroup$
Let's say that we have 10,000 unlabeled documents, and we want to use pool-based sampling with batch size of 5. And we will use least confidence (LC) for querying strategy.
Is this outline of active learning reasonable:
1) Randomly select 30 documents, and get them labelled:=> labelled: 30, unlabelled: 9970.
2) Train the model on 30 labelled data
3) Use the model to make predictions for 9970 unlabelled documents
4) Use LC to select 5 out of all predicted documents that the model is least certain about
5) Get labels for the 5 documents:=> labelled: 35, unlabelled: 9965
6) Retrain the model on labelled data
7) Keep doing steps 2 - 6 until, the labeling resource expires or other stopping criteria is met.
Is this correct? I am mostly concerned about step 3) Should we retrain on all the data on every iteration? It seems like it could get computationally slow, but on the other hand if we don't evaluate every document how would we know which one is the most helpful to get labelled?
machine-learning active-learning
asked yesterday
AkavallAkavall
29017
$endgroup$
Let's say that we have 10,000 unlabeled documents, and we want to use pool-based sampling with batch size of 5. And we will use least confidence (LC) for querying strategy.
Is this outline of active learning reasonable:
1) Randomly select 30 documents, and get them labelled:=> labelled: 30, unlabelled: 9970.
2) Train the model on 30 labelled data
3) Use the model to make predictions for 9970 unlabelled documents
4) Use LC to select 5 out of all predicted documents that the model is least certain about
5) Get labels for the 5 documents:=> labelled: 35, unlabelled: 9965
6) Retrain the model on labelled data
7) Keep doing steps 2 - 6 until, the labeling resource expires or other stopping criteria is met.
Is this correct? I am mostly concerned about step 3) Should we retrain on all the data on every iteration? It seems like it could get computationally slow, but on the other hand if we don't evaluate every document how would we know which one is the most helpful to get labelled?
machine-learning active-learning
machine-learning active-learning
asked yesterday
AkavallAkavall
29017
asked yesterday
AkavallAkavall
29017
asked yesterday
AkavallAkavall
29017
asked yesterday
AkavallAkavall
29017
asked yesterday
AkavallAkavall
29017
29017
add a comment |
add a comment |
1 Answer
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$begingroup$
Your flow is correct. Model is retrained on new labeled data. Otherwise, the next candidates for labeling will be selected from the same region as previous candidates on which the model is least certain. By training on new labeled data, model will move on to new regions about which is least certain. Here is the diagram for pool-based method from page 5 of this survey on active learning:
answered yesterday
P. EsmailianP. Esmailian
1163
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P. Esmailian is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
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$begingroup$
Your flow is correct. Model is retrained on new labeled data. Otherwise, the next candidates for labeling will be selected from the same region as previous candidates on which the model is least certain. By training on new labeled data, model will move on to new regions about which is least certain. Here is the diagram for pool-based method from page 5 of this survey on active learning:
answered yesterday
P. EsmailianP. Esmailian
1163
New contributor
P. Esmailian is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Your flow is correct. Model is retrained on new labeled data. Otherwise, the next candidates for labeling will be selected from the same region as previous candidates on which the model is least certain. By training on new labeled data, model will move on to new regions about which is least certain. Here is the diagram for pool-based method from page 5 of this survey on active learning:
answered yesterday
P. EsmailianP. Esmailian
1163
New contributor
P. Esmailian is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Your flow is correct. Model is retrained on new labeled data. Otherwise, the next candidates for labeling will be selected from the same region as previous candidates on which the model is least certain. By training on new labeled data, model will move on to new regions about which is least certain. Here is the diagram for pool-based method from page 5 of this survey on active learning:
answered yesterday
P. EsmailianP. Esmailian
1163
New contributor
P. Esmailian is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Your flow is correct. Model is retrained on new labeled data. Otherwise, the next candidates for labeling will be selected from the same region as previous candidates on which the model is least certain. By training on new labeled data, model will move on to new regions about which is least certain. Here is the diagram for pool-based method from page 5 of this survey on active learning:
answered yesterday
P. EsmailianP. Esmailian
1163
New contributor
P. Esmailian is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered yesterday
P. EsmailianP. Esmailian
1163
New contributor
P. Esmailian is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered yesterday
P. EsmailianP. Esmailian
1163
answered yesterday
P. EsmailianP. Esmailian
1163
1163
New contributor
P. Esmailian is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
P. Esmailian is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
P. Esmailian is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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