A semi-supervised deep fuzzy C-mean clustering for two classes classification

Ali, Arshad; Riaz, Saman; Jiao, Licheng; Murthy, A. Sampath Dakshina

doi:10.1109/itoec.2017.8122317

Cited by 11 publications

(8 citation statements)

References 15 publications

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“…However, only a limited number of studies have been done on combine feature learning techniques to improve classification performance on the high dimensional and multi-class imbalanced datasets. The classification method proposed in this paper is a new semi-supervised clustering scheme SSFCM that incorporates semi-supervised information in FCM algorithm to considerably improve its effectiveness [22,[62][63][64][65][66][67][68][69]. More details about the feature learning techniques can be found in the article by Jiang et al [70], in which they combined several feature extraction methods with a support vector machine classifier to group the vehicles in six categories, namely "large bus", "passenger car", "motorcycle", "minibus", "truck" and "van".…”

Section: Related Workmentioning

confidence: 99%

Robust Vehicle Classification Based on Deep Features Learning

2021

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This paper aims to introduce a scientific Semi-Supervised Fuzzy C-Mean (SSFCM) clustering approach for passenger cars classification based on the feature learning technique. The proposed method is able to classify passenger vehicles in the micro, small, middle, upper middle, large and luxury classes. The performance of the algorithm is analyzed and compared with an unsupervised fuzzy C-means (FCM) clustering algorithm and Swiss expert classification dataset. Experiment results demonstrate that the classification of SSFCM algorithm had better correlation with expert classification than traditional unsupervised algorithm. These results exhibit that SSFCM can reduce the sensitivity of FCM to the initial cluster centroids with the help of labeled instances. Furthermore, SSFCM results in improved classification performance by using the resampling technique to deal with the multi-class imbalanced problem and eliminate the irrelevant and redundant features.

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Section: Related Workmentioning

confidence: 99%

Robust Vehicle Classification Based on Deep Features Learning

2021

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“…A reconstruction mechanism is used to regularize the autoencoder. Deep fuzzy c-means (DFCM) [28] is a deep learningbased algorithm that combines deep learning and Fuzzy C-Mean. DFCM achieved a performance of 48.65% and 64.54% in terms of ARI and NMI, respectively, in the Fashion-MNIST dataset and 68.15% and 76.36% in terms of ARI and NMI, respectively, in the USPS dataset.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Landmark-Based Spectral Clustering for Big Datasets

2021

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Clustering has emerged as an effective tool for the processing and assessment of the vast data generated by modern applications; its primary aim is to classify data into clusters in which the items are grouped into a given category. However, various challenges, such as volume, velocity, and variety, occur during the clustering of big data. Different algorithms have been proposed to enhance the performance of clustering. The landmark-based spectral clustering (LSC) technique has been proven to be efficient in clustering big datasets. In this study, an algorithm called adaptive landmark-based spectral clustering (ALSC) is proposed for clustering big datasets. The proposed algorithm comprises the adaptive competitive learning neural network (ACLNN) algorithm, which can be efficiently used to determine the number of clusters and the LSC technique. The ACLNN algorithm can also be used with small datasets. Thus, in our implementation, the original big dataset is split into N small subdatasets, which run in parallel by N copies of the ACLNN algorithm. To evaluate the performance of the proposed algorithm, two distinctive datasets, namely, Fashion-MNIST and United States Postal Service are used. The experiments show that the proposed ALSC algorithm produces high clustering accuracy with the identification of the number of clusters. Results reveal that the normalized mutual information and adjusted Rand index of the proposed algorithm outperform state-of-the-art models.INDEX TERMS Adaptive competitive learning neural network, big datasets, clustering; landmark-based spectral clustering, parallel processing.

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“…In this paper, we design a new approach for multi-class imbalanced data classification, namely DFCM-MC, which is the extension of our previous work [23]- [25]. We extend our previous work for the binary imbalanced dataset to the multiclass imbalanced dataset by utilizing decomposition strategy on two layers.…”

Section: Introductionmentioning

confidence: 97%

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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A semi-supervised deep fuzzy C-mean clustering for two classes classification

Cited by 11 publications

References 15 publications

Robust Vehicle Classification Based on Deep Features Learning

Robust Vehicle Classification Based on Deep Features Learning

Adaptive Landmark-Based Spectral Clustering for Big Datasets

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

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