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Classification Recognition Algorithm Based on Strong Association Rule Optimization of Neural Network

Journal from gdlhub / 2016-11-07 06:49:57
Oleh : Zhang Xuewu, Joern Huenteler, Telkomnika
Dibuat : 2016-06-01, dengan 1 file

Keyword : Text classification; Feature generating; Weight calculation; Feature clustering; Information entropy
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/4364

Feature selection of text is one of the basic matters for intelligent classification of text. Textual feature generating algorithm adopts weighted textual vector space model generally at present. This model uses BP network evaluation function to calculate weight value of single feature and textual feature redundancy generated in this algorithm is high generally. For this problem, a textual feature generating algorithm based on clustering weighting is adopted. This new method conducts initial weighted treatment for feature candidate set first of all and then conducts further weighted treatment of features through semantic and information entropy and it removes redundancy features with features clustering at last. Experiment shows that the average classification accuracy rate of this algorithm is about 5% higher than that of traditional BP network algorithm.

Deskripsi Alternatif :

Feature selection of text is one of the basic matters for intelligent classification of text. Textual feature generating algorithm adopts weighted textual vector space model generally at present. This model uses BP network evaluation function to calculate weight value of single feature and textual feature redundancy generated in this algorithm is high generally. For this problem, a textual feature generating algorithm based on clustering weighting is adopted. This new method conducts initial weighted treatment for feature candidate set first of all and then conducts further weighted treatment of features through semantic and information entropy and it removes redundancy features with features clustering at last. Experiment shows that the average classification accuracy rate of this algorithm is about 5% higher than that of traditional BP network algorithm.

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