Title | ||
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Learning Dynamic Simultaneous Clustering And Classification Via Automatic Differential Evolution And Firework Algorithm |
Abstract | ||
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Semi-supervised learning is significant data analysis method in the age of Big Data. The Bayesian-based classifier is a classical classification method in semi-supervised learning. Wherein, the classifier with clustering and classification technology have experienced the transformation from sequential structure to simultaneous structure. There are two main difficulties in simultaneous structure: the limited accuracy and diversity caused by rigid optimization algorithm; and the imbalance status between clustering and classification processes caused by insufficiently structure. To overcome these difficulties, a novel multi-objective differential evolution and firework algorithm for automatic simultaneous clustering and classification algorithm (MASCC-DE/FWA) is proposed. The main contributions of MASCC-DE/FWA contain: (1) Combination searching strategy for dynamic searching (2) Rapid and low-complexity silhouette coefficient as redesigned clustering objective function (3) Automatic clustering, opposition-based learning and adjusted mutual information for strengthening SCC-MOEA framework. The experimental result demonstrates that MASCC-DE/FWA performs better than other 8 state-of-art classification algorithms on synthetic dataset, 19 UCI datasets and image segmentation tasks. (C) 2020 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
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2020 | 10.1016/j.asoc.2020.106593 | APPLIED SOFT COMPUTING |
Keywords | DocType | Volume |
Clustering, Classification, Multi-objective optimization | Journal | 96 |
ISSN | Citations | PageRank |
1568-4946 | 2 | 0.38 |
References | Authors | |
0 | 3 |
Name | Order | Citations | PageRank |
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Haoran Li | 1 | 12 | 1.00 |
Fazhi He | 2 | 540 | 41.02 |
Yi-Lin Chen | 3 | 144 | 9.13 |