Abstract
This study used call logs collected from smartphone application system to establish an classifier, to classify the unwanted phone calls including: Fraud, Sales and Trick. Each phone number was classified by user’s subjective comments. And we used the class label and attributes from the call log data to train the classifier, which can differentiate normal calls and abnormal calls.The attributes we used in the training model were based on the reference and the expert opinions. We wrote a parser to read original call log data, and specified the attributes we need. The attributes we defined can give more detail description to the original data, make it easier to find the behavior mode of the calls.To find the behavior of each type of calls, various statistical analyses were performed. The analyses results can be applied to establish the classifier for differentiating different type of calls.The classifier of this study can be applied in mobile phone as a warning mechanism. If a coming call is predicted as abnormal call, then the system will give a warning message. The continuous collection of the user’s subjective comments, can be very helpful to improve the effectiveness of the classifier and the alarming system.