# A Machine Learning based Robust Prediction Model for Real-life Mobile Phone Data

## TL;DR

This paper introduces a robust machine learning prediction model for real-life mobile phone data that effectively identifies and removes noisy instances to enhance prediction accuracy, using a dynamic noise threshold and decision trees.

## Contribution

The paper presents a novel noise filtering approach using naive Bayes and Laplace estimator tailored to individual user behavior, improving mobile data prediction models.

## Key findings

- Improved precision, recall, and F-measure on real mobile datasets.
- Effective noise removal enhances model accuracy.
- Decision tree classifier performs well on cleaned data.

## Abstract

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.