Classification problems with uneven class distributions present several difficulties during the training as well as during the evaluation process of classifiers. A classification problem with such characteristics has resulted from a data-mining project where the objective was to predict customer insolvency. Using the dataset from the customer insolvency problem we study several alternative methodologies which have been reported to better suit the specific characteristics of this type of problems. Three different but equally important directions are examined; (a) the performance measures that should be used for problems in this domain, (b) the class distributions that should be used for the training data sets, (c) the classification algorithms to be used. The final evaluation of the resulting classifiers is based on a study of the economic impact of classification results. This study concludes to a framework that provides the "best" classifiers, identifies the performance measures that should be used as the decision criterion and suggests the "best" class distribution based on the value of the relative gain from correct classification in the positive class.This framework has been applied in the customer insolvency problem, but it is claimed that it can be applied to many similar problems with uneven class distributions that almost always require a multi-objective evaluation proces.

No abstract

No abstract

Although problem-based learning (PBL) has many advantages, it often fails to connect to the real world outside the classroom. The integration with the laboratory setting and the use of information and communication technologies (ICTs) have been proposed to address this deficiency. Multi-user virtual environments (MUVEs) like Second Life (SL) are 3D collaborative virtual environments that could act as complementary or alternative worlds for the implementation of laboratory PBL activities offering low-cost, safe, and always available environments. The aim of this study was to compare a simple laboratory PBL activity implemented in both the real and virtual worlds, in terms of learning outcome, satisfaction, and presence. The sample consisted of 150 undergraduate university students. The results show that the MUVE provided similar learning outcome and satisfaction to the real-world condition. Presence was positively correlated to satisfaction but not to the learning outcome. Finally, there are indications that the MUVE was perceived as more pleasurable and informal learning environment, while reality was perceived as more stressful.

No abstract