At present, nuisance SMS is getting more and more intense, and its forms are diversified, hidden and malicious. Not only does it waste cell phone resources, but it also affects people's normal use of cell phones. If the situation is bad, it will also affect the normal social order. In this paper, we use Word2Vec algorithm and LightGBM algorithm to select feature words and build a nuisance SMS identification model. By using the classical nuisance SMS training set for training, the results show that our model has an accuracy rate of over 99%. And our model has higher performance compared with the popular classification models.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.