Multi-label text classification (3 targetted result columns) The 2019 Stack Overflow Developer Survey Results Are InText-Classification-Problem, what is the right approach?RTextTools multi-label classificationMulti-label Text ClassificationHow to use binary relevance for multi-label text classification?Naive Bayes for Multi label text classificationExtract Pattern using Short Text ProcessingBuild train data set for natural language text classification?Large Numpy.Array for Multi-label Image Classification (CelebA Dataset)Multi Class Classification on large dataset with over 600 classesHow to use sklearn train_test_split to stratify data for multi-label classification?
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Multi-label text classification (3 targetted result columns)
The 2019 Stack Overflow Developer Survey Results Are InText-Classification-Problem, what is the right approach?RTextTools multi-label classificationMulti-label Text ClassificationHow to use binary relevance for multi-label text classification?Naive Bayes for Multi label text classificationExtract Pattern using Short Text ProcessingBuild train data set for natural language text classification?Large Numpy.Array for Multi-label Image Classification (CelebA Dataset)Multi Class Classification on large dataset with over 600 classesHow to use sklearn train_test_split to stratify data for multi-label classification?
$begingroup$
I am trying to build a multi-label text classifier for suggesting "AC_location", "Issue" and "Part_Affected" on the generated events.
The textual data is full of noise.
The approach I have been following until now was a BOW approach with Tf-idf weighting (almost all the preprocessing steps i used so far like; stopwords removal, stemming, etc.). However, I could only figure it out how to apply a multi-class text classification approach on targetted result column "Issue" and reached an accuracy of around 0.55 on the test set using a random forest approach.
How to apply machine learning for other targetted result columns simultaneously?
I am looking specifically on how to apply machine learning for multi-label.
I have 20, 25 and 30 labels respectively and around 20000 data points. Data-set is not well-balanced but i will make it balance in future.
How to apply text classification for multiple target result columns?
PS: - i'm writing code in R by using the following libraries; tm, mlr, ranger, xgboost, etc.
machine-learning r text-mining random-forest multilabel-classification
edited Mar 30 at 2:47
Damini Jain
1136
asked Mar 29 at 13:08
Ahsan NawazAhsan Nawaz
62
$endgroup$
add a comment |
$begingroup$
I am trying to build a multi-label text classifier for suggesting "AC_location", "Issue" and "Part_Affected" on the generated events.
The textual data is full of noise.
The approach I have been following until now was a BOW approach with Tf-idf weighting (almost all the preprocessing steps i used so far like; stopwords removal, stemming, etc.). However, I could only figure it out how to apply a multi-class text classification approach on targetted result column "Issue" and reached an accuracy of around 0.55 on the test set using a random forest approach.
How to apply machine learning for other targetted result columns simultaneously?
I am looking specifically on how to apply machine learning for multi-label.
I have 20, 25 and 30 labels respectively and around 20000 data points. Data-set is not well-balanced but i will make it balance in future.
How to apply text classification for multiple target result columns?
PS: - i'm writing code in R by using the following libraries; tm, mlr, ranger, xgboost, etc.
machine-learning r text-mining random-forest multilabel-classification
edited Mar 30 at 2:47
Damini Jain
1136
asked Mar 29 at 13:08
Ahsan NawazAhsan Nawaz
62
$endgroup$
$begingroup$
have you tried Deep Learning based approach ? With tools like Ludwig, you can try this approach without significant amount of coding. github.com/uber/ludwig . user guide has an example on text classification : uber.github.io/ludwig/user_guide
$endgroup$
– Shamit Verma
Mar 29 at 13:25
add a comment |
$begingroup$
I am trying to build a multi-label text classifier for suggesting "AC_location", "Issue" and "Part_Affected" on the generated events.
The textual data is full of noise.
The approach I have been following until now was a BOW approach with Tf-idf weighting (almost all the preprocessing steps i used so far like; stopwords removal, stemming, etc.). However, I could only figure it out how to apply a multi-class text classification approach on targetted result column "Issue" and reached an accuracy of around 0.55 on the test set using a random forest approach.
How to apply machine learning for other targetted result columns simultaneously?
I am looking specifically on how to apply machine learning for multi-label.
I have 20, 25 and 30 labels respectively and around 20000 data points. Data-set is not well-balanced but i will make it balance in future.
How to apply text classification for multiple target result columns?
PS: - i'm writing code in R by using the following libraries; tm, mlr, ranger, xgboost, etc.
machine-learning r text-mining random-forest multilabel-classification
edited Mar 30 at 2:47
Damini Jain
1136
asked Mar 29 at 13:08
Ahsan NawazAhsan Nawaz
62
$endgroup$
I am trying to build a multi-label text classifier for suggesting "AC_location", "Issue" and "Part_Affected" on the generated events.
The textual data is full of noise.
The approach I have been following until now was a BOW approach with Tf-idf weighting (almost all the preprocessing steps i used so far like; stopwords removal, stemming, etc.). However, I could only figure it out how to apply a multi-class text classification approach on targetted result column "Issue" and reached an accuracy of around 0.55 on the test set using a random forest approach.
How to apply machine learning for other targetted result columns simultaneously?
I am looking specifically on how to apply machine learning for multi-label.
I have 20, 25 and 30 labels respectively and around 20000 data points. Data-set is not well-balanced but i will make it balance in future.
How to apply text classification for multiple target result columns?
PS: - i'm writing code in R by using the following libraries; tm, mlr, ranger, xgboost, etc.
machine-learning r text-mining random-forest multilabel-classification
machine-learning r text-mining random-forest multilabel-classification
edited Mar 30 at 2:47
Damini Jain
1136
asked Mar 29 at 13:08
Ahsan NawazAhsan Nawaz
62
edited Mar 30 at 2:47
Damini Jain
1136
asked Mar 29 at 13:08
Ahsan NawazAhsan Nawaz
62
edited Mar 30 at 2:47
Damini Jain
1136
edited Mar 30 at 2:47
Damini Jain
1136
edited Mar 30 at 2:47
Damini Jain
1136
1136
asked Mar 29 at 13:08
Ahsan NawazAhsan Nawaz
62
asked Mar 29 at 13:08
Ahsan NawazAhsan Nawaz
62
asked Mar 29 at 13:08
Ahsan NawazAhsan Nawaz
62
62
$begingroup$
have you tried Deep Learning based approach ? With tools like Ludwig, you can try this approach without significant amount of coding. github.com/uber/ludwig . user guide has an example on text classification : uber.github.io/ludwig/user_guide
$endgroup$
– Shamit Verma
Mar 29 at 13:25
add a comment |
$begingroup$
have you tried Deep Learning based approach ? With tools like Ludwig, you can try this approach without significant amount of coding. github.com/uber/ludwig . user guide has an example on text classification : uber.github.io/ludwig/user_guide
$endgroup$
– Shamit Verma
Mar 29 at 13:25
$begingroup$
have you tried Deep Learning based approach ? With tools like Ludwig, you can try this approach without significant amount of coding. github.com/uber/ludwig . user guide has an example on text classification : uber.github.io/ludwig/user_guide
$endgroup$
– Shamit Verma
Mar 29 at 13:25
$begingroup$
have you tried Deep Learning based approach ? With tools like Ludwig, you can try this approach without significant amount of coding. github.com/uber/ludwig . user guide has an example on text classification : uber.github.io/ludwig/user_guide
$endgroup$
– Shamit Verma
Mar 29 at 13:25
add a comment |
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$begingroup$
have you tried Deep Learning based approach ? With tools like Ludwig, you can try this approach without significant amount of coding. github.com/uber/ludwig . user guide has an example on text classification : uber.github.io/ludwig/user_guide
$endgroup$
– Shamit Verma
Mar 29 at 13:25