Improving Semi-Supervised Classification using Clustering

Detecting Concept Drift in Data Stream Using Semi-Supervised Classification

Data stream is a sequence of data generated from various information sources at a high speed and high volume. Classifying data streams faces the three challenges of unlimited length, online processing, and concept drift. In related research, to meet the challenge of unlimited stream length, commonly the stream is divided into fixed size windows or gradual forgetting is used. Concept drift refer...

Using clustering analysis to improve semi-supervised classification

Semi-supervised classification has become an active topic recently and a number of algorithms, such as Self-training, have been proposed to improve the performance of supervised classification using unlabeled data. In this paper, we propose a semi-supervised learning framework which combines clustering and classification. Our motivation is that clustering analysis is a powerful knowledge-discov...

Semi-supervised Spectral Clustering for Classification

We propose a Classification Via Clustering (CVC) algorithm which enables existing clustering methods to be efficiently employed in classification problems. In CVC, training and test data are coclustered and class-cluster distributions are used to find the label of the test data. To determine an efficient number of clusters, a Semi-supervised Hierarchical Clustering (SHC) algorithm is proposed. ...

Multi-label ASRS Dataset Classification Using Semi Supervised Subspace Clustering

There has been a lot of research targeting text classification. Many of them focus on a particular characteristic of text data multi-labelity. This arises due to the fact that a document may be associated with multiple classes at the same time. The consequence of such a characteristic is the low performance of traditional binary or multi-class classification techniques on multi-label text data....

Semi-supervised Clustering Using Heterogeneous Dissimilarities