  The presence of noisy instances in mobile phone data is a fundamental issue
for classifying user phone call behavior (i.e., accept, reject, missed and
outgoing), with many potential negative consequences. The classification
accuracy may decrease and the complexity of the classifiers may increase due to
the number of redundant training samples. To detect such noisy instances from a
training dataset, researchers use naive Bayes classifier (NBC) as it identifies
misclassified instances by taking into account independence assumption and
conditional probabilities of the attributes. However, some of these
misclassified instances might indicate usages behavioral patterns of individual
mobile phone users. Existing naive Bayes classifier based noise detection
techniques have not considered this issue and, thus, are lacking in
classification accuracy. In this paper, we propose an improved noise detection
technique based on naive Bayes classifier for effectively classifying users'
phone call behaviors. In order to improve the classification accuracy, we
effectively identify noisy instances from the training dataset by analyzing the
behavioral patterns of individuals. We dynamically determine a noise threshold
according to individual's unique behavioral patterns by using both the naive
Bayes classifier and Laplace estimator. We use this noise threshold to identify
noisy instances. To measure the effectiveness of our technique in classifying
user phone call behavior, we employ the most popular classification algorithm
(e.g., decision tree). Experimental results on the real phone call log dataset
show that our proposed technique more accurately identifies the noisy instances
from the training datasets that leads to better classification accuracy.
