Improvement of Pre-processing Capacity of Support Vector Clustering using Neural Network Kernel Function for Stream Data Classification

Recently, it has been found that e-commerce (EC) websites provide a large amount of useful information that exceed the human cognitive processing capacity. In order to help customers in comparing alternatives when buying a product, previous research authors have designed opinion summarization systems based on customer reviews. They ignored the template information provided by manufacturers, although its descriptive information has the most useful product characteristics and texts are linguistically correct, unlike reviews. Therefore, this paper proposes a methodology coined as SEOpinion (summarization and exploration of opinions) to summarize aspects and spot opinion(s) regarding them using a combination of template information with customer reviews in two main phases. First, the hierarchical aspect extraction (HAE) phase creates a hierarchy of aspects from the template. Subsequently, the hierarchical aspect-based opinion summarization (HAOS) phase enriches this hierarchy with customers’ opinions to be shown to other potential buyers. To test the feasibility of using deep learning-based BERT techniques with our approach, we created a corpus by gathering information from the top five EC websites for laptops. The experimental results showed that recurrent neural network (RNN) achieved better results (77.4% and 82.6% in terms of F1-measure for the first and second phases, respectively) than the convolutional neural network (CNN) and the support vector machine (SVM) technique.

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Sparse Twin Support Vector Clustering using Pinball Loss

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2021.3059910 ◽

2021 ◽

pp. 1-1 ◽

Cited By ~ 1

Author(s):

M. Tanveer ◽

Tarun Gupta ◽

Miten Shah ◽

Bharat Richhariya

Keyword(s):

Support Vector ◽

Support Vector Clustering ◽

Pinball Loss ◽

Vector Clustering

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Pinball Loss Twin Support Vector Clustering

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3409264 ◽

2021 ◽

Vol 17 (2s) ◽

pp. 1-23

Author(s):

M. Tanveer ◽

Tarun Gupta ◽

Miten Shah ◽

Keyword(s):

Loss Function ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Structural Mri ◽

Twin Support Vector Machine ◽

Support Vector ◽

Support Vector Clustering ◽

Hinge Loss ◽

Pinball Loss ◽

Vector Clustering

Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.

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