Pedro de Almeida

Supporting Fraud Analysis in Mobile Telecommunications Using Case-Based Reasoning

Fraud in mobile telecommunications is a complex and dynamic problem for Telecom operators. These companies have developed and are exploring new ways of making the fraud detection process more efficient. Most of these attempts are based in fraud management systems, capable of detecting fraudulent communications. In this paper, we present a case-based reasoning system that aids fraud analysts in the classification of potential fraud cases. The system developed, presents to the analyst the most similar past cases, representing suspicious communication episodes that were previously investigated. We also describe an example of how the system is used.

The Use of Domain Knowledge in Feature Construction for Financial Time Series Prediction

Most of the existing data mining approaches to time series prediction use as training data an embed of the most recent values of the time series, following the traditional linear auto-regressive methodologies. However, in many time series prediction tasks the alternative approach that uses derivative features constructed from the raw data with the help of domain theories can produce significant prediction accuracy improvements. This is particularly noticeable when the available data includes multivariate information although the aim is still the prediction of one particular time series. This latter situation occurs frequently in financial time series prediction. This paper presents a method of feature construction based on domain knowledge that uses multivariate time series information. We show that this method improves the accuracy of next-day stock quotes prediction when compared with the traditional embed of historical values extracted from the original data.

Direct Domain Knowledge Inclusion in the PA3 Rule Induction Algorithm

Inclusion of domain knowledge in a process of knowledge discovery in databases is a complex but very important part of successful knowledge discovery solutions. In real-life data mining development, nonstructured domain knowledge involvement in the data preparation phase and in the final interpretation/evaluation phase tends to dominate. This paper presents an experiment of direct domain knowledge integration in the algorithm that will search for interesting patterns in the data. In the context of stock market prediction work, a recent rule induction algorithm, PA3, was adapted to include domain theories directly in the internal rule development. Tests performed over several Portuguese stocks show a significant increase in prediction performance over the same process using the standard version of PA3. We believe that a similar methodology can be applied to other symbolic induction algorithms and in other working domains to improve the efficiency of prediction (or classification) in knowledge-intensive data mining tasks.