Fuzzy C-Means Clustering  Based on Improved Marked Watershed Transformation

Zhao, Cuijie; Zhao, Hongdong; Yao, Wei

doi:10.12928/telkomnika.v14i3.2757

Cited by 2 publications

(1 citation statement)

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“…To increase grouping efficiency and solving problems with proximity issues, the semi-supervised FCM clustering approach may be a better choice for an imbalanced class problem. Many researchers have proposed Semi-supervised FCM based algorithms for classification and clustering [32], [34], [43].…”

Section: Related Workmentioning

confidence: 99%

Semi-Supervised Deep Fuzzy C-Mean Clustering for Imbalanced Multi-Class Classification

2019

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Semi-supervised learning has been successfully connected in the research fields of machine learning such as data mining and dynamic data analysis. Imbalance class learning is one of the most challenging issues for classification. In recent years, the core focal point of numerous researchers has been on data classification of multi-class imbalanced datasets. In this paper, we proposed semi-supervised deep Fuzzy C-mean clustering for imbalanced multi-class classification (DFCM-MC). In our paper, the word ''Deep'' is used to show how decomposition strategy is applied deeply, first, decomposes the original semi-supervised data into supervised (labeled) and unsupervised (unlabeled) data. For training the model, we used unlabeled data along with labeled data to extract discriminative information, which is useful for classification. Second, it further decomposes the supervised and unsupervised data into multi intra-cluster that to address the problem of multi-class imbalance data, which tends to maximize intra-cluster classes and intra-cluster features. We propose a novel approach DFCM-MC by utilizing multi-intra clusters to extract new features to control redundancy for multi-class imbalance classification, which associates the maximum similarity of features between multi-intra clusters. Furthermore, we improve the classification performance of the DFCM-MC, apply the re-sampling technique to handle the imbalance data for classification. We conduct our experiments on 18 benchmark multi-class imbalanced datasets to demonstrate the performance of our proposed approach with the four state-of-the-art learning algorithms for multi-class imbalance data with three performance measures (mean of accuracy, mean of f-measure, and mean of area under the curve). The experiment results demonstrate that our proposed approach performs better due to their capacity to recognize and consolidate fundamental information from unsupervised data. INDEX TERMS Semi-supervised learning, imbalanced data, multi-class classification, Fuzzy C-mean clustering, and feature learning.

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Section: Related Workmentioning

confidence: 99%