Improving Semi-Supervised Classification using Clustering

Supervised classification techniques, broadly depend on the availability of labeled data. However, collecting this labeled data is always a tedious and costly process. To reduce these efforts and improve the performance of classification process, this paper proposes a new framework, which combines a...

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Bibliographic Details
Published in:EAI endorsed transactions on scalable information systems 2020-03, Vol.7 (25), p.159793
Main Authors: Arora, J., Tushir, M., Kashyap, R.
Format: Article
Language:English
Subjects:
Algorithms
Classification
Clustering
fuzzy c- means
naive bayes classification
Performance enhancement
Performance evaluation
probability
semi-supervised clustering
Online Access:Get full text
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Summary:Supervised classification techniques, broadly depend on the availability of labeled data. However, collecting this labeled data is always a tedious and costly process. To reduce these efforts and improve the performance of classification process, this paper proposes a new framework, which combines a most basic classification technique with the semi-supervised process of clustering. Semi-supervised clustering algorithms, aim to increase the accuracy of clustering process by effectively exploring available supervision from a limited amount of labeled data and help to label the unlabeled data. In our paper, a semi-supervised clustering is integrated with naive bayes classification technique which helps to better train the classifier. To evaluate the performance of the proposed technique, we conduct experiments on several real world benchmark datasets. The experimental results show that the proposed approach surpasses the competing approaches in both accuracy and efficiency.
ISSN:2032-9407
2032-9407
DOI:10.4108/eai.29-7-2019.159793