Classification Recognition Algorithm Based on Strong Association Rule Optimization of Neural Network
Abstract: 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.
Keywords: Text classification;
Feature generating; Weight calculation; Feature clustering; Information entropy
Author: Zhang Xuewu, Joern
Huenteler
Journal Code: jptkomputergg160056
