A Framework for Context-Aware Semi Supervised Learning

A Framework for Context-Aware Semi Supervised Learning

Vijaya Geeta Dharmavaram

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Shashi Mogalla

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GITAM University

A Framework for Context-Aware Semi Supervised Learning

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Abstract

Supervised learning techniques require large number of labeled examples to build a classifier which is often difficult and expensive to collect. Unsupervised learning techniques, even though do not require labeled examples often form clusters regardless of the intended purpose or context. The authors proposes a semi supervised learning framework that leverages the large number of unlabeled examples in addition to limited number of labeled examples to form clusters as per the context. This framework also supports the development of semi supervised classifier based on the proximity of unknown example to the clusters so formed. The authors proposes a new algorithm namely “Semi Supervised Relevance Feature Estimation”, (SFRE), to identify the relevant features along with their significance weightages which is integrated with the proposed framework.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Vijaya Geeta Dharmavaram. 2014. \u201cA Framework for Context-Aware Semi Supervised Learning\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C1).

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GJCST Volume 14 Issue C1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

context – aware discriminant analysis feature relevance semi supervised learning subspace clustering

Submission ReceivedDecember 14, 2013
Peer Review Double Blind
Handling Editor
Accepted January 4, 2014
Published January 15, 2014

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v1.2

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May 14, 2014

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Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Vijaya Geeta Dharmavaram, Shashi Mogalla (PhD/Dr. count: 0)

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Total Views: 8858

Total Downloads: 2394

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