Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization
Classification predicts classes of objects using the knowledge learned during the training phase. This process requires learning from labeled samples. However, the labeled samples usually limited. Annotation process is annoying, tedious, expensive, and requires human experts. Meanwhile, unlabeled data is available and almost free. Semi-supervised learning approaches make use of both labeled and unlabeled data. This paper introduces cluster and label approach using PSO for semi-supervised classification. PSO is competitive to traditional clustering algorithms. A new local best PSO is presented to cluster the unlabeled data. The available labeled data guides the learning process. The experiments are conducted using four state-of-the-art datasets from different domains. The results compared with Label Propagation a popular semi-supervised classifier and two state-of-the-art supervised classification models, namely k-nearest neighbors and decision trees. The experiments show the efficiency of the proposed model.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationClusteringGeneral ClassificationSimilar Papers 제목 키워드 기반
Particle Competition and Cooperation for Semi-Supervised Learning with Label Noise
Semi-supervised learning methods are usually employed in the classification of data sets where only a small subset of the data items is labeled. In these scenarios, label noise is a crucial issue, since the noise may eas…
ClassificationGeneral ClassificationA Semi-Supervised Self-Organizing Map for Clustering and Classification
There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples. Semi-supervised learning…
ClusteringGeneral ClassificationRethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering
The emergence of graph neural networks (GNNs) has offered a powerful tool for semi-supervised node classification tasks. Subsequent studies have achieved further improvements through refining the message passing schemes …
Node ClassificationData AugmentationGraph ClusteringBeyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclustering
A long-standing issue with deep learning is the need for large and consistently labeled datasets. Although the current research in semi-supervised learning can decrease the required amount of annotated data by a factor o…
General Classificationimage-classificationImage ClassificationA Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints
In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose SSC into two simpler clas…
Binary ClassificationClassificationClusteringGeneral Classification