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LPU
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LPU

Overview
Synopsis

LPU (which stands for Learning from Positive and Unlabeled data) is a text learning or classification system that learns from a set of positive documents and a set of unlabeled documents (without labeled negative documents).

Category

Text Analytics Software Free

Features

•Cost sensitive classification
• Ramp loss function
• Hinge loss function

License

Open Source

Price

Free

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Website
Company

LPU

What is best?

•Cost sensitive classification
• Ramp loss function
• Hinge loss function

What are the benefits?

•Can be used for both retrieval or classification.

PAT Rating™ ( Beta)
Editor Rating
Aggregated User Rating
Rate Here
Ease of use
7.6
7.2
Features & Functionality
7.6
6.9
Advanced Features
7.6
Integration
7.6
Performance
7.6
Customer Support
7.6
Implementation
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Bottom Line

LPU is a text learning or classification system that learns from a set of positive documents and a set of unlabeled documents (without labeled negative documents) and can be used for both retrieval or classification.

7.6
Editor Rating
7.1
Aggregated User Rating
1 rating
You have rated this

LPU (which stands for Learning from Positive and Unlabeled data) is a text learning or classification system that learns from a set of positive documents and a set of unlabeled documents (without labeled negative documents). This type of learning is different from classic text learning/classification, in which both positive and negative training documents are required. Given a set of positive documents and a set of unlabeled documents, the LPU algorithm learns a classifier in two steps:

• Step 1 : Identifying a set of reliable negative documents from the unlabeled set. For this step, LPU has three techniques, i.e., spy, roc (rocchio), nb (naive bayes) and DNF. In all these techniques, the unlabeled set is treated as negative data. In Spy technique you sample a certain % of positive examples and put them into unlabeled set to act as “spies”.Then run a classification algorithm assuming all unlabeled examples are negative, we will know the behavior of those actual positive examples in the unlabeled set through the “spies”.We can then extract reliable negative examples from the unlabeled set more accurance.
• Step 2 : Building and selecting a classifier, which consists of two sub-steps:
1. Building a set of classifiers by iteratively applying a classification algorithm. For this step, LPU has two techniques, SVM(Support vector machines ) and EM (Expectation Maximization).
2. Selecting a good classifier from the set of classifiers constructed above. We call this sub-step "catching a good classifier".
LPU system can be used for retrieval or classification. For retrieval, the document collection is the unlabeled set, which is also the test set. For classification, you can provide a separate test set that is different from the unlabeled set used in training.

 

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Ease of use
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