  Real-life mobile phone data may contain noisy instances, which is a
fundamental issue for building a prediction model with many potential negative
consequences. The complexity of the inferred model may increase, may arise
overfitting problem, and thereby the overall prediction accuracy of the model
may decrease. In this paper, we address these issues and present a robust
prediction model for real-life mobile phone data of individual users, in order
to improve the prediction accuracy of the model. In our robust model, we first
effectively identify and eliminate the noisy instances from the training
dataset by determining a dynamic noise threshold using naive Bayes classifier
and laplace estimator, which may differ from user-to-user according to their
unique behavioral patterns. After that, we employ the most popular rule-based
machine learning classification technique, i.e., decision tree, on the
noise-free quality dataset to build the prediction model. Experimental results
on the real-life mobile phone datasets (e.g., phone call log) of individual
mobile phone users, show the effectiveness of our robust model in terms of
precision, recall and f-measure.
