A Particle Swarm Optimized Learning Model of Fault Classification in Web Apps

Описание: Title: A Particle Swarm Optimized Learning Model of Fault Classification in Web-Apps

Domain: Data Mining

Key Features:

1. Finding faults in such dynamic content is challenging, as to whether the fault is exposed or not depends on its execution path. Moreover, the complexity and uniqueness of each web application make fault assessment an extremely laborious and expensive task.

2. Five supervised learning algorithms, namely, naïve Bayesian, decision tree, support vector machine, K-nearest neighbors and multi-layer perception (neural network) have been implemented and analyzed to and the best predictive learning model for fault classification.

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