A New Data-Driven Deep Learning Model for Pattern Categorization using Fast Independent Component Analysis and Radial Basis Function Network. Taking Social Networks resources as a case

This paper investigates the categorization problem using Data Mining techniques. We present a new conceptual model, which is named FICARBFN, for classifying patterns by using Fast Fixed-Point Algorithm for Independent Component Analysis and Radial Basis Function Network. It uses an artificial neural...

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Bibliographic Details
Published in:Procedia computer science 2017, Vol.113, p.97-104
Main Authors: Djellali, Choukri, Adda, Mehdi
Format: Article
Language:English
Subjects:
Categorization
Data Mining
Deep learning
Factor analysis
Pattern Recognition
Variables Selection
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Summary:This paper investigates the categorization problem using Data Mining techniques. We present a new conceptual model, which is named FICARBFN, for classifying patterns by using Fast Fixed-Point Algorithm for Independent Component Analysis and Radial Basis Function Network. It uses an artificial neural network model to find a single consolidated categorization, which is composed of tree process, variables selection, categorization, and finally models selection. Our categorization model used a hybrid technique that combines the advantages of factorial analysis and Neural Network approaches. Comparative study and experimental results showed that our scheme optimized the bias-variance on the selected model and achieved an enhanced generalization for Social Networks patterns recognition.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2017.08.320