You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@lucene.apache.org by "Cao Manh Dat (JIRA)" <ji...@apache.org> on 2016/07/06 07:47:11 UTC
[jira] [Assigned] (SOLR-9252) Feature selection and logistic
regression on text
[ https://issues.apache.org/jira/browse/SOLR-9252?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Cao Manh Dat reassigned SOLR-9252:
----------------------------------
Assignee: Cao Manh Dat
> Feature selection and logistic regression on text
> -------------------------------------------------
>
> Key: SOLR-9252
> URL: https://issues.apache.org/jira/browse/SOLR-9252
> Project: Solr
> Issue Type: Improvement
> Security Level: Public(Default Security Level. Issues are Public)
> Reporter: Cao Manh Dat
> Assignee: Cao Manh Dat
> Attachments: SOLR-9252.patch, SOLR-9252.patch, enron1.zip
>
>
> SOLR-9186 come up with a challenges that for each iterative we have to rebuild the tf-idf vector for each documents. It is costly computation if we represent doc by a lot of terms. Features selection can help reducing the computation.
> Due to its computational efficiency and simple interpretation, information gain is one of the most popular feature selection methods. It is used to measure the dependence between features and labels and calculates the information gain between the i-th feature and the class labels (http://www.jiliang.xyz/publication/feature_selection_for_classification.pdf).
> I confirmed that by running logistics regressions on enron mail dataset (in which each email is represented by top 100 terms that have highest information gain) and got the accuracy by 92% and precision by 82%.
> This ticket will create two new streaming expression. Both of them use the same *parallel iterative framework* as SOLR-8492.
> {code}
> featuresSelection(collection1, q="*:*", field="tv_text", outcome="out_i", positiveLabel=1, numTerms=100)
> {code}
> featuresSelection will emit top terms that have highest information gain scores. It can be combined with new tlogit stream.
> {code}
> tlogit(collection1, q="*:*",
> featuresSelection(collection1,
> q="*:*",
> field="tv_text",
> outcome="out_i",
> positiveLabel=1,
> numTerms=100),
> field="tv_text",
> outcome="out_i",
> maxIterations=100)
> {code}
> In the iteration n, the text logistics regression will emit nth model, and compute the error of (n-1)th model. Because the error will be wrong if we compute the error dynamically in each iteration.
> In each iteration tlogit will change learning rate based on error of previous iteration. It will increase the learning rate by 5% if error is going down and It will decrease the learning rate by 50% if error is going up.
> This will support use cases such as building models for spam detection, sentiment analysis and threat detection.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: dev-unsubscribe@lucene.apache.org
For additional commands, e-mail: dev-help@lucene.apache.org