Abstract

Abstract

Abstract
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).

Abstract

Abstract
This paper presents a tool for automating the design of road tunnels lighting systems. The tunnel lighting system must guarantee some minimal luminance values in order to ensure a easy driving and visual perception. The lights distribution, in different tunnel zones, is obtained in the proposed technique by using a genetic algorithm. The developed software framework automatically selects the best light type and its localization, according to a specified design objective, along the tunnel independently of the light manufacturer. © 2009 IEEE.

Abstract
This paper presents and evaluates an approach to novelty detection that addresses it as the problem of identifying novel concepts in a continuous learning scenario, as an extension to a single-class classification problem. OLINDDA, an OnLIne Novelty and Drift Detection Algorithm that implements this approach, uses efficient standard clustering algorithms to continuously generate candidate clusters among examples that were not explained by the current known concepts. Clusters complying with a validation criterion that takes cohesiveness and representativeness into account are initially identified as concepts. By merging similar concepts, OLINDDA may enhance the representation of some concepts as it advances toward its final goal of describing novel emerging concepts in an unsupervised way. The proposed approach is experimentally evaluated by the use of several measures taken throughout the learning process. Results show that it is capable of identifying novel concepts that are pure and correspond to real classes, disregarding unrepresentative clusters and outliers.