Abstract

Abstract
Text Categorization (TC) has attracted the attention of the research community in the last decade. Algorithms like Support Vector Machines, Naïve Bayes or k Nearest Neighbors have been used with good performance, confirmed by several comparative studies. Recently, several ensemble classifiers were also introduced in TC. However, many of those can only provide a category for a given new sample. Instead, in this paper, we propose a methodology - MECAC - to build an ensemble of classifiers that has two advantages to other ensemble methods: 1) it can be run using parallel computing, saving processing time and 2) it can extract important statistics from the obtained clusters. It uses the mean co-association matrix to solve binary TC problems. Our experiments revealed that our framework performed, on average, 2.04% better than the best individual classifier on the tested datasets. These results were statistically validated for a significance level of 0.05 using the Friedman Test. © 2012 Springer-Verlag.

Abstract
In recent years, both companies and researchers have been exploring intelligent data analysis to increase the profitability of the taxi industry. Intelligent systems for online taxi dispatching and time saving route finding have been built to do so. In this paper, we propose a novel methodology to produce online predictions regarding the spatial distribution of passenger demand throughout taxi stand networks. We have done so by assembling two well-known time series short-term forecast models: the time-varying Poisson models and ARIMA models. Our tests were performed using data gathered over a period of 6 months and collected from 63 taxi stands within the city of Porto, Portugal. Our results demonstrate that this model is a true major contribution to the driver mobility intelligence: 78% of the 253745 demanded taxi services were correctly forecasted in a 30 minutes horizon. © Springer-Verlag Berlin Heidelberg 2012.

Abstract
Nowadays, Informed Driving is crucial to the transportation industry. We present an online recommendation model to help the driver to decide about the best stand to head in each moment, minimizing the waiting time. Our approach uses time series forecasting techniques to predict the spatiotemporal distribution in real-time. Then, we combine this information with the live current network status to produce our output. Our online test-beds were carried out using data obtained from a fleet of 441 vehicles running in the city of Porto, Portugal. We demonstrate that our approach can be a major contribution to this industry: 395.361/506.873 of the services dispatched were correctly predicted. Our tests also highlighted that a fleet equipped with such framework surpassed a fleet that is not: they experienced an average waiting time to pick-up a passenger 5% lower than its competitor.

Abstract
This chapter presents an adaptive predictive model for a student modeling prediction task in the context of an adaptive educational hypermedia system (AEHS). The task, that consists in determining what kind of learning resources are more appropriate to a particular learning style, presents two issues that are critical. The first is related to the uncertainty of the information about the student's learning style acquired by psychometric instruments. The second is related to the changes over time of the student's preferences (concept drift). To approach this task, we propose a probabilistic adaptive predictive model that includes a method to handle concept drift based on statistical quality control. We claim that our approach is able to adapt quickly to changes in the student's preferences and that it should be successfully used in similar user modeling prediction tasks, where uncertainty and concept drift are presented. © 2006, Idea Group Inc.

Abstract
In highly populated urban zones, it is common to notice headway deviations (HD) between pairs of buses. When these events occur in a bus stop, they often cause bus bunching (BB) in the following bus stops. Several proposals have been suggested to mitigate this problem. In this paper, we propose to find BBS (Bunching Black Spots) - sequences of bus stops where systematic HD events cause the formation of BB. We run a sequence mining algorithm, named PrefixSpan, to find interesting events available in time series. We prove that we can accurately model the BB trip usual pattern like a frequent sequence mining problem. The subsequences proved to be a promising way of identify the route' schedule points to adjust in order to mitigate such events. © 2012 Springer-Verlag.