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Supervised learning on sources of information with different importance
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWhat algorithms should I use to perform job classification based on resume data?How to extract features and classify alert emails coming from monitoring tools into proper category?Question and Answer Chatbot for Customer SupportWhich approach for user classification on chat text (classifier, representation, features)?Best approach for image recognition/classification with few training dataData amount for a very simple chatbotUsing neural networks for classification in Hierarchical dataHow to set the parameters of a Hidden Markov Model that'll be used to correct the mistakes by a previous classifier?Visitor's probability to purchase on eCommerce site, based on aggregate historic dataautoencoder for features selection
$begingroup$
I am trying to classify customer support sessions using supervised machine learning.
In each customer support session I have 3 bags of information.
1. The title of the customer's complaint
2. Information about the device the customer was using
3. Text of the chat session with the customer support agent
In each customer support session, there are 6 different classes. Is it better to:
1. Train a classifier on each bag of information and have them vote on which class the session belongs to?
2. Put all of the information into a single set of features, and train a single classifier to determine which class the session belongs to?
3. Other?
machine-learning nlp feature-selection aggregation
asked Dec 29 '17 at 6:11
TobyToby
462
$endgroup$
bumped to the homepage by Community♦ 42 mins 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 |
$begingroup$
I am trying to classify customer support sessions using supervised machine learning.
In each customer support session I have 3 bags of information.
1. The title of the customer's complaint
2. Information about the device the customer was using
3. Text of the chat session with the customer support agent
In each customer support session, there are 6 different classes. Is it better to:
1. Train a classifier on each bag of information and have them vote on which class the session belongs to?
2. Put all of the information into a single set of features, and train a single classifier to determine which class the session belongs to?
3. Other?
machine-learning nlp feature-selection aggregation
asked Dec 29 '17 at 6:11
TobyToby
462
$endgroup$
bumped to the homepage by Community♦ 42 mins 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 |
$begingroup$
I am trying to classify customer support sessions using supervised machine learning.
In each customer support session I have 3 bags of information.
1. The title of the customer's complaint
2. Information about the device the customer was using
3. Text of the chat session with the customer support agent
In each customer support session, there are 6 different classes. Is it better to:
1. Train a classifier on each bag of information and have them vote on which class the session belongs to?
2. Put all of the information into a single set of features, and train a single classifier to determine which class the session belongs to?
3. Other?
machine-learning nlp feature-selection aggregation
asked Dec 29 '17 at 6:11
TobyToby
462
$endgroup$
I am trying to classify customer support sessions using supervised machine learning.
In each customer support session I have 3 bags of information.
1. The title of the customer's complaint
2. Information about the device the customer was using
3. Text of the chat session with the customer support agent
In each customer support session, there are 6 different classes. Is it better to:
1. Train a classifier on each bag of information and have them vote on which class the session belongs to?
2. Put all of the information into a single set of features, and train a single classifier to determine which class the session belongs to?
3. Other?
machine-learning nlp feature-selection aggregation
machine-learning nlp feature-selection aggregation
asked Dec 29 '17 at 6:11
TobyToby
462
asked Dec 29 '17 at 6:11
TobyToby
462
asked Dec 29 '17 at 6:11
TobyToby
462
asked Dec 29 '17 at 6:11
TobyToby
462
asked Dec 29 '17 at 6:11
TobyToby
462
462
bumped to the homepage by Community♦ 42 mins 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♦ 42 mins 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 |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Its better to do supervised classification then unsupervised clustering since you have the right labels for the response.
Now in supervised classification approach 1 is better than approach 2 because in approach 2 you will the reducing the already available information by combining features.
Approach 3 is advised where you will create more features from the given 3 features by use of NLP processing, sentiments, tags, chunking, time taken, text length etc and then selecting the features those provide more information gain using feature selection approaches.
answered Dec 29 '17 at 10:33
gopal krishna varshneygopal krishna varshney
541
$endgroup$
add a comment |
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1 Answer
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1 Answer
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$begingroup$
Its better to do supervised classification then unsupervised clustering since you have the right labels for the response.
Now in supervised classification approach 1 is better than approach 2 because in approach 2 you will the reducing the already available information by combining features.
Approach 3 is advised where you will create more features from the given 3 features by use of NLP processing, sentiments, tags, chunking, time taken, text length etc and then selecting the features those provide more information gain using feature selection approaches.
answered Dec 29 '17 at 10:33
gopal krishna varshneygopal krishna varshney
541
$endgroup$
add a comment |
$begingroup$
Its better to do supervised classification then unsupervised clustering since you have the right labels for the response.
Now in supervised classification approach 1 is better than approach 2 because in approach 2 you will the reducing the already available information by combining features.
Approach 3 is advised where you will create more features from the given 3 features by use of NLP processing, sentiments, tags, chunking, time taken, text length etc and then selecting the features those provide more information gain using feature selection approaches.
answered Dec 29 '17 at 10:33
gopal krishna varshneygopal krishna varshney
541
$endgroup$
add a comment |
$begingroup$
Its better to do supervised classification then unsupervised clustering since you have the right labels for the response.
Now in supervised classification approach 1 is better than approach 2 because in approach 2 you will the reducing the already available information by combining features.
Approach 3 is advised where you will create more features from the given 3 features by use of NLP processing, sentiments, tags, chunking, time taken, text length etc and then selecting the features those provide more information gain using feature selection approaches.
answered Dec 29 '17 at 10:33
gopal krishna varshneygopal krishna varshney
541
$endgroup$
Its better to do supervised classification then unsupervised clustering since you have the right labels for the response.
Now in supervised classification approach 1 is better than approach 2 because in approach 2 you will the reducing the already available information by combining features.
Approach 3 is advised where you will create more features from the given 3 features by use of NLP processing, sentiments, tags, chunking, time taken, text length etc and then selecting the features those provide more information gain using feature selection approaches.
answered Dec 29 '17 at 10:33
gopal krishna varshneygopal krishna varshney
541
answered Dec 29 '17 at 10:33
gopal krishna varshneygopal krishna varshney
541
answered Dec 29 '17 at 10:33
gopal krishna varshneygopal krishna varshney
541
answered Dec 29 '17 at 10:33
gopal krishna varshneygopal krishna varshney
541
541
add a comment |
add a comment |
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