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 Sequential clustering and classification using deep learning technique and multi-objective sine-cosine algorithm
Tác giả hoặc Nhóm tác giả: R.J. Kuo, Muhammad Rakhmat Setiawan, Thi Phuong Quyen Nguyen*
Nơi đăng: Computers & Industrial Engineering; Số: Vol. 173;Từ->đến trang: 1-15;Năm: 2022
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
This study introduced a novel data analytics-based sequential clustering and classification (SCC) approach. The proposed approach named deep MOSCA-SCC integrates a multi-objective sine–cosine algorithm (MOSCA), deep clustering technique, and classification algorithms to exploit data structure before implementing the prediction model. Herein, the autoencoder combined with the K-means algorithm was utilized for the deep clustering to reveal the data pattern. Regarding classification, support vector machine, back-propagation neural network, and decision tree classification were implemented to explore the correlated factors with the revealed patterns. To evaluate the performance of the proposed method, a comparison was conducted between the proposed deep MOSCA-SCC and other benchmark algorithms, including the NSGAII-SCC and the regular MOSCA-SCC. The deep MOSCA-SCC achieved better performance than other algorithms in terms of clustering sum of squared error and classification accuracy. In addition, deep MOSCA-SCC with a decision tree yielded the best accuracies with 92.6% and 87% when the numbers of clusters were equal to 3 and 4, respectively.
ABSTRACT
This study introduced a novel data analytics-based sequential clustering and classification (SCC) approach. The proposed approach named deep MOSCA-SCC integrates a multi-objective sine–cosine algorithm (MOSCA), deep clustering technique, and classification algorithms to exploit data structure before implementing the prediction model. Herein, the autoencoder combined with the K-means algorithm was utilized for the deep clustering to reveal the data pattern. Regarding classification, support vector machine, back-propagation neural network, and decision tree classification were implemented to explore the correlated factors with the revealed patterns. To evaluate the performance of the proposed method, a comparison was conducted between the proposed deep MOSCA-SCC and other benchmark algorithms, including the NSGAII-SCC and the regular MOSCA-SCC. The deep MOSCA-SCC achieved better performance than other algorithms in terms of clustering sum of squared error and classification accuracy. In addition, deep MOSCA-SCC with a decision tree yielded the best accuracies with 92.6% and 87% when the numbers of clusters were equal to 3 and 4, respectively.
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