A novel approach for distance-based semi-supervised clustering using functional link neural network

Chandra, B.; Gupta, Manish

doi:10.1007/s00500-012-0912-7

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…2) It is hard for SVDD to determine the number of hyperspheres adaptively. 1) Clustering-Based Classification Models: Numerous studies revealed that there is a connection between clustering and classification [17], [20], [21]. Such studies include radial basis function networks (RBFNs) [20] and functional link neural network [21].…”

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confidence: 99%

“…1) Clustering-Based Classification Models: Numerous studies revealed that there is a connection between clustering and classification [17], [20], [21]. Such studies include radial basis function networks (RBFNs) [20] and functional link neural network [21]. RBFN is a clustering-classification style neural network classifier and has its incremental version IRBFN, but the number of clusters is fixed and this limits its adaptivity [20].…”

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confidence: 99%

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Classifying With Adaptive Hyper-Spheres: An Incremental Classifier Based on Competitive Learning

Kou

Peng

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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Nowadays, datasets are always dynamic and patterns in them are changing. Instances with different labels are intertwined and often linearly inseparable, which bring new challenges to traditional learning algorithms. This paper proposes adaptive hyper-sphere (AdaHS), an adaptive incremental classifier, and its kernelized version: Nys-AdaHS. The classifier incorporates competitive training with a border zone. With adaptive hidden layer and tunable radii of hyper-spheres, AdaHS has strong capability of local learning like instance-based algorithms, but free from slow searching speed and excessive memory consumption. The experiments showed that AdaHS is robust, adaptive, and highly accurate. It is especially suitable for dynamic data in which patterns are changing, decision borders are complicated, and instances with the same label can be spherically clustered.

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Classifying With Adaptive Hyper-Spheres: An Incremental Classifier Based on Competitive Learning

Kou

Peng

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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“…Chandra and Gupta proposed a novel distance-based semi-supervised clustering algorithm functional link neural network (FLNN) [18]. The motivation of their work is to overcome the disadvantages of the pair-wise constrains, which is the base for most of the semi-supervised clustering techniques.…”

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confidence: 99%

KNN Classifier and Naive Bayse Classifier for Crime Prediction in San Francisco Context

Abdulrahman¹,

Abedalkhader²

2017

IJDMS

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GACH: a grid-based algorithm for hierarchical clustering of high-dimensional data

Mansoori

2013

Soft Comput

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A novel approach for distance-based semi-supervised clustering using functional link neural network

Cited by 7 publications

References 27 publications

Classifying With Adaptive Hyper-Spheres: An Incremental Classifier Based on Competitive Learning

Classifying With Adaptive Hyper-Spheres: An Incremental Classifier Based on Competitive Learning

KNN Classifier and Naive Bayse Classifier for Crime Prediction in San Francisco Context

GACH: a grid-based algorithm for hierarchical clustering of high-dimensional data

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