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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shang-Chia Wei Tso-Jung Yen Wei-Chang Yeh |
| Copyright Year | 2015 |
| Description | Author affiliation: Inst. of Stat. Sci., Taipei, Taiwan (Shang-Chia Wei; Tso-Jung Yen) || Dept. of Ind. Eng. & Eng. Manage., Nat. Tsing Hua Univ., Hsinchu, Taiwan (Wei-Chang Yeh) |
| Abstract | Hierarchical fuzzy aggregation network (HFAN) is a fine multilayer information fusion system that carries out multi-criteria aggregation. It can be regarded as a functional classifier for dealing with decision-making problems. The HFAN comprises fuzzy aggregation operators built from adjusted parameters (γ) and associated weights (δ). In this paper, we adopt soft computing techniques (e.g., PSO and SSO) to learn these fuzzy aggregation operators. We provide association rules to define input data and use a hierarchical clustering algorithm to organize the network structure. The optimization efficiency of these rules is experimented with different network topologies and datasets. We verify effectiveness of HFAN by applying it to classify the breast cancer dataset from the UCI Machine Learning Repository. We conduct study for comparing the optimized HFAN and other approaches in terms of ten-fold cross-validation. |
| Starting Page | 2705 |
| Ending Page | 2712 |
| File Size | 612493 |
| Page Count | 8 |
| File Format | |
| e-ISBN | 9781479974924 |
| DOI | 10.1109/CEC.2015.7257224 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-05-25 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Optimization Accuracy Particle swarm optimization Association rules Classification algorithms Clustering algorithms Breast cancer ten-fold cross-validation Hierarchical Fuzzy Aggregation Network Soft Computing Associattion Rules Hierarchical Clustering Algorithm Breast Cancer dataset |
| Content Type | Text |
| Resource Type | Article |
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