Abstract
We present a case study in which sequence mining algorithms were applied to web access log data. The data are from a portal that is targeted for business users. In this portal, like in many others, content is described using a set of descriptors, such as keywords, category and type. We investigate whether representing content by the type rather than its identifier enables existing sequence mining methods to obtain interesting patterns. Rather than a more traditional approach based on measures such as support and confidence, we analyze results from an application perspective. This enables us to identify opportunities for improving and extending these methods.

Abstract

Abstract

Abstract
The problem of learning rankings is receiving increased attention from several research communities. In this paper we empirically evaluate an adaptation of the algorithm of learning decision trees for rankings. Our experiments are carried out on some metalearning problems, which consist of relating characteristics of learning problems to the relative performance of learning algorithms. We obtain positive results which, somewhat surprisingly, indicate that the method predicts more accurately the top ranks. Copyright © 2008, Association for the Advancement of Artificial Intelligence.

Abstract
Due to the wide variety of algorithms for supervised classification originating from several research areas, selecting one of them to apply on a given problem is not a trivial task. Recently several methods have been developed to create rankings of classification algorithms based on their previous performance. Therefore, it is necessary to develop techniques to evaluate and compare those methods. We present three measures to evaluate rankings of classification algorithms, give examples of their use and discuss their characteristics.

Abstract
Long-term travel time prediction (TTP) can be an important planning tool for both freight transport and public transport companies. In both cases it is expected that the use of long-term TTP can improve the quality of the planned services by reducing the error between the actual and the planned travel times. However, for reasons that we try to stretch out along this paper, long-term TTP is almost not mentioned in the scientific literature. In this paper we discuss the relevance of this study and compare three non-parametric state-of-the-art regression methods: Projection Pursuit Regression (PPR), Support Vector Machine (SVM) and Random Forests (RF). For each one of these methods we study the best combination of input parameters. We also study the impact of different methods for the pre-processing tasks (feature selection, example selection and domain values definition) in the accuracy of those algorithms. We use bus travel time's data from a bus dispatch system. From an off-the-shelf point-of-view, our experiments show that RF is the most promising approach from the three we have tested. However, it is possible to obtain more accurate results using PPR but with extra pre-processing work, namely on example selection and domain values definition.