Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure predict...

Adapting transmission parameters to the future channel state is an appealing approach to improve efficiency in wireless communication. Adaptation requires predicting the channel state. Current channel-adaptive techniques assume that the prediction is perfect. In this paper, we claim that neglecting the prediction error can lead to poor performance results, possibly even worse than without predi...

Web prefetching techniques have been widely used to reduce the latency perceived by users. An important part of these techniques is the prediction algorithm whose main aim is to predict the future users’ accesses based on the previous visiting patterns. For this purpose, the algorithms proposed in the open literature usually consider a training period before making predictions. The length of th...

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Classimbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the...

Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this article, we study both supervised and unsupervised link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heteroge...

A wide range of classification models have been explored for financial risk prediction, but conclusions on which technique behaves better may vary when different performance evaluation measures are employed. Accordingly, this paper proposes the use of multiple criteria decision making tools in order to give a ranking of algorithms. More specifically, the selection of the most appropriate credit...