How can I know the name of the features selected by a Deep Belief Network?How deep should my neural network be?Clustering for multiple variablefeature names in LogisticRegression()Handling a combined dataset of numerical and categorical features for Regressionautoencoder for features selectionTraining of word weights in Word Embedding and Word2VecHow to generate a sample from a generative model like a Restricted Boltzmann Machine?How to deal with Nominal categorical with label encoding?Response variable is nominal.After running K-means on 12 features, I get an array containing clusters for each row. What is the next step after this?
Is it the same airport YUL and YMQ in Canada?
Was Hulk present at this event?
How to scale a verbatim environment on a minipage?
Point of the the Dothraki's attack in GoT S8E3?
Accidentally deleted the "/usr/share" folder
How do you center multiple equations that have multiple steps?
How did Arya manage to disguise herself?
How to reply this mail from potential PhD professor?
Attending a conference where my ex-supervisor and his collaborator are present, should I attend?
Why is the SNP putting so much emphasis on currency plans?
Disabling Resource Governor in SQL Server
How to creep the reader out with what seems like a normal person?
Missed the connecting flight
How do I tell my manager that his code review comment is wrong?
nth number which is divisible by all the numbers from 1 to 10
Packet sniffer for MacOS Mojave and above
Does the Darkness spell dispel the Color Spray and Flaming Sphere spells?
Entropy as a function of temperature: is temperature well defined?
Feels like I am getting dragged into office politics
Why is Arya visibly scared in the library in S8E3?
What happens if I start too many background jobs?
Selecting a secure PIN for building access
Stark VS Thanos
Was Unix ever a single-user OS?
How can I know the name of the features selected by a Deep Belief Network?
How deep should my neural network be?Clustering for multiple variablefeature names in LogisticRegression()Handling a combined dataset of numerical and categorical features for Regressionautoencoder for features selectionTraining of word weights in Word Embedding and Word2VecHow to generate a sample from a generative model like a Restricted Boltzmann Machine?How to deal with Nominal categorical with label encoding?Response variable is nominal.After running K-means on 12 features, I get an array containing clusters for each row. What is the next step after this?
$begingroup$
I want to use DBN to reduce the 41 features of nslkdd dataset after transforming nominal data to numeric the number of features increases from 41 to 121 . I used 3 RBMs (121-50-10) now I want to know the 10 selected features i.e know their names to put them as an input to the classifier. how can I do it?
python deep-learning keras tensorflow rbm
edited Jun 12 '18 at 18:56
n1k31t4
6,8412422
asked Jun 12 '18 at 10:21
useruser
4917
$endgroup$
add a comment |
$begingroup$
I want to use DBN to reduce the 41 features of nslkdd dataset after transforming nominal data to numeric the number of features increases from 41 to 121 . I used 3 RBMs (121-50-10) now I want to know the 10 selected features i.e know their names to put them as an input to the classifier. how can I do it?
python deep-learning keras tensorflow rbm
edited Jun 12 '18 at 18:56
n1k31t4
6,8412422
asked Jun 12 '18 at 10:21
useruser
4917
$endgroup$
$begingroup$
I have edited your question a little to make it easier to read. In general, it is helpful to include a little more information to allow people to understand the context. E.g. I did not know what the NSLKDD dataset was. Links are also good, but your question should contain enough information to get an answer, minimising time users must spend looking at other resources.
$endgroup$
– n1k31t4
Jun 12 '18 at 10:44
add a comment |
$begingroup$
I want to use DBN to reduce the 41 features of nslkdd dataset after transforming nominal data to numeric the number of features increases from 41 to 121 . I used 3 RBMs (121-50-10) now I want to know the 10 selected features i.e know their names to put them as an input to the classifier. how can I do it?
python deep-learning keras tensorflow rbm
edited Jun 12 '18 at 18:56
n1k31t4
6,8412422
asked Jun 12 '18 at 10:21
useruser
4917
$endgroup$
I want to use DBN to reduce the 41 features of nslkdd dataset after transforming nominal data to numeric the number of features increases from 41 to 121 . I used 3 RBMs (121-50-10) now I want to know the 10 selected features i.e know their names to put them as an input to the classifier. how can I do it?
python deep-learning keras tensorflow rbm
python deep-learning keras tensorflow rbm
edited Jun 12 '18 at 18:56
n1k31t4
6,8412422
asked Jun 12 '18 at 10:21
useruser
4917
edited Jun 12 '18 at 18:56
n1k31t4
6,8412422
asked Jun 12 '18 at 10:21
useruser
4917
edited Jun 12 '18 at 18:56
n1k31t4
6,8412422
edited Jun 12 '18 at 18:56
n1k31t4
6,8412422
edited Jun 12 '18 at 18:56
n1k31t4
6,8412422
6,8412422
asked Jun 12 '18 at 10:21
useruser
4917
asked Jun 12 '18 at 10:21
useruser
4917
asked Jun 12 '18 at 10:21
useruser
4917
4917
$begingroup$
I have edited your question a little to make it easier to read. In general, it is helpful to include a little more information to allow people to understand the context. E.g. I did not know what the NSLKDD dataset was. Links are also good, but your question should contain enough information to get an answer, minimising time users must spend looking at other resources.
$endgroup$
– n1k31t4
Jun 12 '18 at 10:44
add a comment |
$begingroup$
I have edited your question a little to make it easier to read. In general, it is helpful to include a little more information to allow people to understand the context. E.g. I did not know what the NSLKDD dataset was. Links are also good, but your question should contain enough information to get an answer, minimising time users must spend looking at other resources.
$endgroup$
– n1k31t4
Jun 12 '18 at 10:44
$begingroup$
I have edited your question a little to make it easier to read. In general, it is helpful to include a little more information to allow people to understand the context. E.g. I did not know what the NSLKDD dataset was. Links are also good, but your question should contain enough information to get an answer, minimising time users must spend looking at other resources.
$endgroup$
– n1k31t4
Jun 12 '18 at 10:44
$begingroup$
I have edited your question a little to make it easier to read. In general, it is helpful to include a little more information to allow people to understand the context. E.g. I did not know what the NSLKDD dataset was. Links are also good, but your question should contain enough information to get an answer, minimising time users must spend looking at other resources.
$endgroup$
– n1k31t4
Jun 12 '18 at 10:44
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
In general you are extracting/creating 10 features that scan theoretically recreate the input i.e. the 41 features. The features on their own may not necessarily make a lot of sense and (depending on the dataset) may not be easily interpretable. One could draw a comparison to Principal Component Analysis and the result components as features.
One benefit of using Deep Belief Nets to pre-train your model and encode features is that the data must not be labelled. This leads to the final point, which is that your 10 features must not necessarily have names. You can just call them e.g. feature1, feature2, ..., feature10.
If you are having problems with the actual code and the way to push the 10 features further into the classifier, I would suggest you provide the code that you have so far and add more detail regarding your exact problem.
answered Jun 12 '18 at 10:55
n1k31t4n1k31t4
6,8412422
$endgroup$
$begingroup$
I want to know the names of the 10 selected feature to remove the rest from my dataset
$endgroup$
– user
Jun 12 '18 at 12:25
1
$begingroup$
The features are not those selected from the original 41, they are combinations of them! You cannot just remove them from the original input.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:34
$begingroup$
ah ok and I must change the nominal features to numeric before putting them to the DBN?
$endgroup$
– user
Jun 12 '18 at 12:40
$begingroup$
Inputs need to be numeric, yes. But that doesn't strictly have anything to do with the actual names themselves - the data itself must be numeric.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:43
$begingroup$
the data must be unlabelled this mean I shoud remove the colomn 'class' from my dataset, how can I add it again to make the classification and the prediction.
$endgroup$
– user
Jun 12 '18 at 13:00
|
show 2 more comments
Your Answer
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "557"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f33011%2fhow-can-i-know-the-name-of-the-features-selected-by-a-deep-belief-network%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
In general you are extracting/creating 10 features that scan theoretically recreate the input i.e. the 41 features. The features on their own may not necessarily make a lot of sense and (depending on the dataset) may not be easily interpretable. One could draw a comparison to Principal Component Analysis and the result components as features.
One benefit of using Deep Belief Nets to pre-train your model and encode features is that the data must not be labelled. This leads to the final point, which is that your 10 features must not necessarily have names. You can just call them e.g. feature1, feature2, ..., feature10.
If you are having problems with the actual code and the way to push the 10 features further into the classifier, I would suggest you provide the code that you have so far and add more detail regarding your exact problem.
answered Jun 12 '18 at 10:55
n1k31t4n1k31t4
6,8412422
$endgroup$
$begingroup$
I want to know the names of the 10 selected feature to remove the rest from my dataset
$endgroup$
– user
Jun 12 '18 at 12:25
1
$begingroup$
The features are not those selected from the original 41, they are combinations of them! You cannot just remove them from the original input.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:34
$begingroup$
ah ok and I must change the nominal features to numeric before putting them to the DBN?
$endgroup$
– user
Jun 12 '18 at 12:40
$begingroup$
Inputs need to be numeric, yes. But that doesn't strictly have anything to do with the actual names themselves - the data itself must be numeric.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:43
$begingroup$
the data must be unlabelled this mean I shoud remove the colomn 'class' from my dataset, how can I add it again to make the classification and the prediction.
$endgroup$
– user
Jun 12 '18 at 13:00
|
show 2 more comments
$begingroup$
In general you are extracting/creating 10 features that scan theoretically recreate the input i.e. the 41 features. The features on their own may not necessarily make a lot of sense and (depending on the dataset) may not be easily interpretable. One could draw a comparison to Principal Component Analysis and the result components as features.
One benefit of using Deep Belief Nets to pre-train your model and encode features is that the data must not be labelled. This leads to the final point, which is that your 10 features must not necessarily have names. You can just call them e.g. feature1, feature2, ..., feature10.
If you are having problems with the actual code and the way to push the 10 features further into the classifier, I would suggest you provide the code that you have so far and add more detail regarding your exact problem.
answered Jun 12 '18 at 10:55
n1k31t4n1k31t4
6,8412422
$endgroup$
$begingroup$
I want to know the names of the 10 selected feature to remove the rest from my dataset
$endgroup$
– user
Jun 12 '18 at 12:25
1
$begingroup$
The features are not those selected from the original 41, they are combinations of them! You cannot just remove them from the original input.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:34
$begingroup$
ah ok and I must change the nominal features to numeric before putting them to the DBN?
$endgroup$
– user
Jun 12 '18 at 12:40
$begingroup$
Inputs need to be numeric, yes. But that doesn't strictly have anything to do with the actual names themselves - the data itself must be numeric.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:43
$begingroup$
the data must be unlabelled this mean I shoud remove the colomn 'class' from my dataset, how can I add it again to make the classification and the prediction.
$endgroup$
– user
Jun 12 '18 at 13:00
|
show 2 more comments
$begingroup$
In general you are extracting/creating 10 features that scan theoretically recreate the input i.e. the 41 features. The features on their own may not necessarily make a lot of sense and (depending on the dataset) may not be easily interpretable. One could draw a comparison to Principal Component Analysis and the result components as features.
One benefit of using Deep Belief Nets to pre-train your model and encode features is that the data must not be labelled. This leads to the final point, which is that your 10 features must not necessarily have names. You can just call them e.g. feature1, feature2, ..., feature10.
If you are having problems with the actual code and the way to push the 10 features further into the classifier, I would suggest you provide the code that you have so far and add more detail regarding your exact problem.
answered Jun 12 '18 at 10:55
n1k31t4n1k31t4
6,8412422
$endgroup$
In general you are extracting/creating 10 features that scan theoretically recreate the input i.e. the 41 features. The features on their own may not necessarily make a lot of sense and (depending on the dataset) may not be easily interpretable. One could draw a comparison to Principal Component Analysis and the result components as features.
One benefit of using Deep Belief Nets to pre-train your model and encode features is that the data must not be labelled. This leads to the final point, which is that your 10 features must not necessarily have names. You can just call them e.g. feature1, feature2, ..., feature10.
If you are having problems with the actual code and the way to push the 10 features further into the classifier, I would suggest you provide the code that you have so far and add more detail regarding your exact problem.
answered Jun 12 '18 at 10:55
n1k31t4n1k31t4
6,8412422
answered Jun 12 '18 at 10:55
n1k31t4n1k31t4
6,8412422
answered Jun 12 '18 at 10:55
n1k31t4n1k31t4
6,8412422
answered Jun 12 '18 at 10:55
n1k31t4n1k31t4
6,8412422
6,8412422
$begingroup$
I want to know the names of the 10 selected feature to remove the rest from my dataset
$endgroup$
– user
Jun 12 '18 at 12:25
1
$begingroup$
The features are not those selected from the original 41, they are combinations of them! You cannot just remove them from the original input.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:34
$begingroup$
ah ok and I must change the nominal features to numeric before putting them to the DBN?
$endgroup$
– user
Jun 12 '18 at 12:40
$begingroup$
Inputs need to be numeric, yes. But that doesn't strictly have anything to do with the actual names themselves - the data itself must be numeric.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:43
$begingroup$
the data must be unlabelled this mean I shoud remove the colomn 'class' from my dataset, how can I add it again to make the classification and the prediction.
$endgroup$
– user
Jun 12 '18 at 13:00
|
show 2 more comments
$begingroup$
I want to know the names of the 10 selected feature to remove the rest from my dataset
$endgroup$
– user
Jun 12 '18 at 12:25
1
$begingroup$
The features are not those selected from the original 41, they are combinations of them! You cannot just remove them from the original input.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:34
$begingroup$
ah ok and I must change the nominal features to numeric before putting them to the DBN?
$endgroup$
– user
Jun 12 '18 at 12:40
$begingroup$
Inputs need to be numeric, yes. But that doesn't strictly have anything to do with the actual names themselves - the data itself must be numeric.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:43
$begingroup$
the data must be unlabelled this mean I shoud remove the colomn 'class' from my dataset, how can I add it again to make the classification and the prediction.
$endgroup$
– user
Jun 12 '18 at 13:00
$begingroup$
I want to know the names of the 10 selected feature to remove the rest from my dataset
$endgroup$
– user
Jun 12 '18 at 12:25
$begingroup$
I want to know the names of the 10 selected feature to remove the rest from my dataset
$endgroup$
– user
Jun 12 '18 at 12:25
1
1
$begingroup$
The features are not those selected from the original 41, they are combinations of them! You cannot just remove them from the original input.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:34
$begingroup$
The features are not those selected from the original 41, they are combinations of them! You cannot just remove them from the original input.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:34
$begingroup$
ah ok and I must change the nominal features to numeric before putting them to the DBN?
$endgroup$
– user
Jun 12 '18 at 12:40
$begingroup$
ah ok and I must change the nominal features to numeric before putting them to the DBN?
$endgroup$
– user
Jun 12 '18 at 12:40
$begingroup$
Inputs need to be numeric, yes. But that doesn't strictly have anything to do with the actual names themselves - the data itself must be numeric.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:43
$begingroup$
Inputs need to be numeric, yes. But that doesn't strictly have anything to do with the actual names themselves - the data itself must be numeric.
$endgroup$
– n1k31t4
Jun 12 '18 at 12:43
$begingroup$
the data must be unlabelled this mean I shoud remove the colomn 'class' from my dataset, how can I add it again to make the classification and the prediction.
$endgroup$
– user
Jun 12 '18 at 13:00
$begingroup$
the data must be unlabelled this mean I shoud remove the colomn 'class' from my dataset, how can I add it again to make the classification and the prediction.
$endgroup$
– user
Jun 12 '18 at 13:00
|
show 2 more comments
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f33011%2fhow-can-i-know-the-name-of-the-features-selected-by-a-deep-belief-network%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
$begingroup$
I have edited your question a little to make it easier to read. In general, it is helpful to include a little more information to allow people to understand the context. E.g. I did not know what the NSLKDD dataset was. Links are also good, but your question should contain enough information to get an answer, minimising time users must spend looking at other resources.
$endgroup$
– n1k31t4
Jun 12 '18 at 10:44