How I can split a fully labeled dataset into labeled and unlabeled data for semi-supervised learning purpose
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I am working on a new idea to improve the classification accuracy in semi-supervised learning. I want to use the same text classification dataset and split this dataset into labeled set and unlabeled set, How I can do that in Java?
Can anyone help me?
java machine-learning weka
asked Jan 4 at 2:35
AmaalAmaal
12
add a comment |
I am working on a new idea to improve the classification accuracy in semi-supervised learning. I want to use the same text classification dataset and split this dataset into labeled set and unlabeled set, How I can do that in Java?
Can anyone help me?
java machine-learning weka
asked Jan 4 at 2:35
AmaalAmaal
12
add a comment |
I am working on a new idea to improve the classification accuracy in semi-supervised learning. I want to use the same text classification dataset and split this dataset into labeled set and unlabeled set, How I can do that in Java?
Can anyone help me?
java machine-learning weka
asked Jan 4 at 2:35
AmaalAmaal
12
I am working on a new idea to improve the classification accuracy in semi-supervised learning. I want to use the same text classification dataset and split this dataset into labeled set and unlabeled set, How I can do that in Java?
Can anyone help me?
java machine-learning weka
java machine-learning weka
asked Jan 4 at 2:35
AmaalAmaal
12
asked Jan 4 at 2:35
AmaalAmaal
12
asked Jan 4 at 2:35
AmaalAmaal
12
asked Jan 4 at 2:35
AmaalAmaal
12
asked Jan 4 at 2:35
AmaalAmaal
12
12
add a comment |
add a comment |
1 Answer
1
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It is not going to give you an improved accuracy when using less labels. If you split you data in order to delete the labels from one group and use that with a semi-supervised learning, it will just reduce your accuracy.
The purpose of semi-supervised is that the process of labeling massive amounts of data for supervised learning is extremely time-consuming and expensive, so if you need more data (that you already have), then you can use techniques to use unlabeled data.
Before even thinking about the coding in Java, can you develop a bit more your idea of why you were thinking of this?
answered Jan 4 at 10:54
NicoNico
488
1
Thanks for your quick response. In my work I try to improve the classification accuracy of the naive Bayes classifier in semi-supervised learning by finding more accurate estimation of the probability terms. I will implement my idea on Bayes classifier and then I will compare my results with the results of the original Bayes classifier to see how my idea will improve the classifier performance. Here, I need a fully labeled dataset to use it as unlabeled data. As I know the true labels of them, I can assess the classification accuracy of my classifier in classifying the unlabeled data
– Amaal
Jan 5 at 5:52
Thanks for your update!
– Nico
Jan 7 at 10:28
add a comment |
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1 Answer
1
active
oldest
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1 Answer
1
active
oldest
votes
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oldest
votes
active
oldest
votes
It is not going to give you an improved accuracy when using less labels. If you split you data in order to delete the labels from one group and use that with a semi-supervised learning, it will just reduce your accuracy.
The purpose of semi-supervised is that the process of labeling massive amounts of data for supervised learning is extremely time-consuming and expensive, so if you need more data (that you already have), then you can use techniques to use unlabeled data.
Before even thinking about the coding in Java, can you develop a bit more your idea of why you were thinking of this?
answered Jan 4 at 10:54
NicoNico
488
1
Thanks for your quick response. In my work I try to improve the classification accuracy of the naive Bayes classifier in semi-supervised learning by finding more accurate estimation of the probability terms. I will implement my idea on Bayes classifier and then I will compare my results with the results of the original Bayes classifier to see how my idea will improve the classifier performance. Here, I need a fully labeled dataset to use it as unlabeled data. As I know the true labels of them, I can assess the classification accuracy of my classifier in classifying the unlabeled data
– Amaal
Jan 5 at 5:52
Thanks for your update!
– Nico
Jan 7 at 10:28
add a comment |
It is not going to give you an improved accuracy when using less labels. If you split you data in order to delete the labels from one group and use that with a semi-supervised learning, it will just reduce your accuracy.
The purpose of semi-supervised is that the process of labeling massive amounts of data for supervised learning is extremely time-consuming and expensive, so if you need more data (that you already have), then you can use techniques to use unlabeled data.
Before even thinking about the coding in Java, can you develop a bit more your idea of why you were thinking of this?
answered Jan 4 at 10:54
NicoNico
488
1
Thanks for your quick response. In my work I try to improve the classification accuracy of the naive Bayes classifier in semi-supervised learning by finding more accurate estimation of the probability terms. I will implement my idea on Bayes classifier and then I will compare my results with the results of the original Bayes classifier to see how my idea will improve the classifier performance. Here, I need a fully labeled dataset to use it as unlabeled data. As I know the true labels of them, I can assess the classification accuracy of my classifier in classifying the unlabeled data
– Amaal
Jan 5 at 5:52
Thanks for your update!
– Nico
Jan 7 at 10:28
add a comment |
It is not going to give you an improved accuracy when using less labels. If you split you data in order to delete the labels from one group and use that with a semi-supervised learning, it will just reduce your accuracy.
The purpose of semi-supervised is that the process of labeling massive amounts of data for supervised learning is extremely time-consuming and expensive, so if you need more data (that you already have), then you can use techniques to use unlabeled data.
Before even thinking about the coding in Java, can you develop a bit more your idea of why you were thinking of this?
answered Jan 4 at 10:54
NicoNico
488
It is not going to give you an improved accuracy when using less labels. If you split you data in order to delete the labels from one group and use that with a semi-supervised learning, it will just reduce your accuracy.
The purpose of semi-supervised is that the process of labeling massive amounts of data for supervised learning is extremely time-consuming and expensive, so if you need more data (that you already have), then you can use techniques to use unlabeled data.
Before even thinking about the coding in Java, can you develop a bit more your idea of why you were thinking of this?
answered Jan 4 at 10:54
NicoNico
488
answered Jan 4 at 10:54
NicoNico
488
answered Jan 4 at 10:54
NicoNico
488
answered Jan 4 at 10:54
NicoNico
488
488
1
Thanks for your quick response. In my work I try to improve the classification accuracy of the naive Bayes classifier in semi-supervised learning by finding more accurate estimation of the probability terms. I will implement my idea on Bayes classifier and then I will compare my results with the results of the original Bayes classifier to see how my idea will improve the classifier performance. Here, I need a fully labeled dataset to use it as unlabeled data. As I know the true labels of them, I can assess the classification accuracy of my classifier in classifying the unlabeled data
– Amaal
Jan 5 at 5:52
Thanks for your update!
– Nico
Jan 7 at 10:28
add a comment |
1
Thanks for your quick response. In my work I try to improve the classification accuracy of the naive Bayes classifier in semi-supervised learning by finding more accurate estimation of the probability terms. I will implement my idea on Bayes classifier and then I will compare my results with the results of the original Bayes classifier to see how my idea will improve the classifier performance. Here, I need a fully labeled dataset to use it as unlabeled data. As I know the true labels of them, I can assess the classification accuracy of my classifier in classifying the unlabeled data
– Amaal
Jan 5 at 5:52
Thanks for your update!
– Nico
Jan 7 at 10:28
1
1
Thanks for your quick response. In my work I try to improve the classification accuracy of the naive Bayes classifier in semi-supervised learning by finding more accurate estimation of the probability terms. I will implement my idea on Bayes classifier and then I will compare my results with the results of the original Bayes classifier to see how my idea will improve the classifier performance. Here, I need a fully labeled dataset to use it as unlabeled data. As I know the true labels of them, I can assess the classification accuracy of my classifier in classifying the unlabeled data
– Amaal
Jan 5 at 5:52
Thanks for your quick response. In my work I try to improve the classification accuracy of the naive Bayes classifier in semi-supervised learning by finding more accurate estimation of the probability terms. I will implement my idea on Bayes classifier and then I will compare my results with the results of the original Bayes classifier to see how my idea will improve the classifier performance. Here, I need a fully labeled dataset to use it as unlabeled data. As I know the true labels of them, I can assess the classification accuracy of my classifier in classifying the unlabeled data
– Amaal
Jan 5 at 5:52
Thanks for your update!
– Nico
Jan 7 at 10:28
Thanks for your update!
– Nico
Jan 7 at 10:28
add a comment |
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