Semi-supervised Deep Fuzzy C-mean Clustering For Imbalanced Multi-class Classification

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

Abstract:

Semi-supervised learning is a class of machine learning tasks and techniques that also make use of unlabeled data for training typically a small amount of labeled data with a large amount of unlabeled data. Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy. Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. Fuzzy c-means has been a very important tool for image processing, bioinformatics in clustering objects. 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.