Detecting Errors in Foreign Trade Transactions: Dealing with Insufficient Data

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Luis Torgo (Author) FCUP; Welma Pereira (Author) Other; Carlos Soares (Author) FEP

Conference proceedings International

Title: Progress in Artificial Intelligence: Proceedings of the 14th Portuguese Conference on Artificial Intelligence, EPIA2009

Pages: 435-446

14th EPIA - Portuguese Conference on Artificial Intelligence - EPIA 2009

Universidade de Aveiro, Portugal, 12 a 15 de Outubro de 2009

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-003-RW4

DOI: 10.1007/978-3-642-04686-5_36

Abstract (EN): This paper describes a data mining approach to the problem of detecting erroneous foreign trade transactions in data collected by the Portuguese Institute of Statistics (INE). Erroneous transactions are a minority, but still they have an important impact: on the official statistics produced by INE. Detecting these rare errors is a manual, time-consuming task, which is constrained by a limited amount of available resources (e.g. financial, human). These constraints are common to many other data analysis problems (e.g. fraud detection). Our previous work addresses this issue by producing a ranking of outlyingness that allows a better management of the available resources by allocating them to the most, relevant cases. It is based on an adaptation of hierarchical clustering methods for outlier detection. However, the method cannot be applied to articles with a small number of transactions. In this paper, we complement the previous approach with some standard statistical methods for outlier detection for handling articles with few transactions. Our experiments clearly show its advantages in terms of the criteria, outlined by INE for considering any method applicable to this business problem. The generality of the approach remains to be tested in other problems which share the same constraints (e.g. fraud detection).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12