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2015 | OriginalPaper | Chapter

Hybrid Approach for Inductive Semi Supervised Learning Using Label Propagation and Support Vector Machine

Authors : Aruna Govada, Pravin Joshi, Sahil Mittal, Sanjay K. Sahay

Published in: Machine Learning and Data Mining in Pattern Recognition

Publisher: Springer International Publishing

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Abstract

Semi supervised learning methods have gained importance in today’s world because of large expenses and time involved in labeling the unlabeled data by human experts. The proposed hybrid approach uses SVM and Label Propagation to label the unlabeled data. In the process, at each step SVM is trained to minimize the error and thus improve the prediction quality. Experiments are conducted by using SVM and logistic regression(Logreg). Results prove that SVM performs tremendously better than Logreg. The approach is tested using 12 datasets of different sizes ranging from the order of 1000s to the order of 10000s. Results show that the proposed approach outperforms Label Propagation by a large margin with F-measure of almost twice on average. The parallel version of the proposed approach is also designed and implemented, the analysis shows that the training time decreases significantly when parallel version is used.

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Metadata
Title
Hybrid Approach for Inductive Semi Supervised Learning Using Label Propagation and Support Vector Machine
Authors
Aruna Govada
Pravin Joshi
Sahil Mittal
Sanjay K. Sahay
Copyright Year
2015
Book
Machine Learning and Data Mining in Pattern Recognition

Print ISBN: 978-3-319-21023-0

Electronic ISBN: 978-3-319-21024-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-21024-7_14

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