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المؤلفRaitoharju, Jenni
المؤلفKiranyaz, Serkan
المؤلفGabbouj, Moncef
تاريخ الإتاحة2021-03-25T11:33:36Z
تاريخ النشر2016
اسم المنشورIEEE Transactions on Neural Networks and Learning Systems
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/TNNLS.2015.2497286
معرّف المصادر الموحدhttp://hdl.handle.net/10576/18014
الملخصIn training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vectors with weighted output vectors (input-output clustering). In this paper, we propose to apply clustering separately for each class (class-specific clustering). The idea has been used in some previous works, but without evaluating the benefits of the approach. We compare the class-specific, input, and input-output clustering approaches in terms of classification performance and computational efficiency when training RBFNNs. To accomplish this objective, we apply three different clustering algorithms and conduct experiments on 25 benchmark data sets. We show that the class-specific approach significantly reduces the overall complexity of the clustering, and our experimental results demonstrate that it can also lead to a significant gain in the classification performance, especially for the networks with a relatively few Gaussian neurons. Among other applied clustering algorithms, we combine, for the first time, a dynamic evolutionary optimization method, multidimensional particle swarm optimization, and the class-specific clustering to optimize the number of cluster centroids and their locations.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعComputational efficiency
Functions
Neural networks
Optimization
Particle swarm optimization (PSO)
Radial basis function networks
Supervised learning
Benchmark data
Classification performance
Clustering methods
Evolutionary optimization method
Input-output clustering
Multi-dimensional particle swarm optimizations
Number of clusters
Radial basis function neural networks
Clustering algorithms
العنوانTraining Radial Basis Function Neural Networks for Classification via Class-Specific Clustering
النوعArticle
الصفحات2458-2471
رقم العدد12
رقم المجلد27
dc.accessType Abstract Only


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