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Integrated Semi-Supervised Model for Learning and Classification

  • Vandna BhallaEmail author
  • Santanu Chaudhury
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)

Abstract

Labelled data are not only time consuming but often expensive and difficult to procure as it involves skilful inputs by humans to tag and annotate. Contrary to this unlabelled data is comparatively easier to procure but fewer methods exist to optimally use them. Semi-Supervised Learning overcomes this problem and assists to build better classifiers by using unlabelled data along with sufficient labelled data and may actually yield higher accuracy with considerably less human input effort. But if the labelled data set is inadequate in size then the Semi-Supervised techniques are also stuck. We propose a novel framework where the small labelled dataset is appropriately augmented using the intelligent learning mechanisms of artificial immune systems to train the proposed model. The model retrains with the unlabelled data to fortify the learning mechanism. We show that the generative deep framework utilizing artificial immune system principles provides a highly competitive approach for learning in the semi-supervised environment.

Keywords

Semi-supervised Self-training Clonal data Integrated 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Indian Institute of Technology DelhiHauz KhasIndia
  2. 2.CEERI PILANIPilaniIndia

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