Abstract
This chapter proposes a new way to detect and represent the probability of ramping events in short-term wind power forecasting. Ramping is one notable characteristic in a time series associated with a drastic change in value in a set of consecutive time steps. Two properties of a ramp event forecast, that is, slope and phase error, are important from the point of view of the system operator (SO): they have important implications in the decisions associated with unit commitment or generation scheduling, especially if there is thermal generation dominance in the power system. Unit commitment decisions, generally taken some 12-48 h in advance, must prepare the generation schedule in order to smoothly accommodate forecasted drastic changes in wind power availability. © Springer India 2013.

Abstract
Usually, during data stream classifier learning, we assume that labels of all incoming examples are available without any delay and they are used to update employing predictive model. Unfortunately, this assumption about access to all class labels is naive and it requires relatively high budget for labeling. It causes that methods which can train data stream classifiers on the basis of partially labeled data are highly desirable. Among them, active learning [1] seems to be a promising direction, which focuses on selecting only the most valuable learning examples to be labeled and used to produce an accurate predictive model. However, designing such a system we have to ensure that a cho-sen active learning strategy is able to handle changes in data distribution and quickly adapt to changing data distribution. In this work, we focus on novel active learning strategies that are designed for effective tackling of such changes. We propose a novel active data stream classifier learning method based on query by clustering approach. Experimental evaluation of the proposed methods prove the usefulness of the proposed approach for reducing labeling cost for classifier of drifting data streams.

Abstract

Abstract
Different activities are performed by people during the day and many aspects of life are associated with places of human mobility patterns. Among those activities, there are some that are recurrent and demand displacement of the individual between regular places like going to work, going to school, going back home from wherever the individual is located. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics. In this paper, we propose a method for discovering common pathways across users’ habits. By using density-based clustering algorithms, we detect the users’ most preferable locations and apply a Gaussian Mixture Model (GMM) over these locations to automatically separate the trajectories that follow patterns of days and hours, in order to discover the representations of individual’s habits. Over the set of users’ habits, we search for the trajectories that are more common among them by using the Longest Common Sub-sequence (LCSS) algorithm considering the distance that pairs of users travel on the same path. To evaluate the proposed method we use a real-world GPS dataset. The results show that the method is able to find common routes between users that have similar habits paving the way for future recommendation, prediction and carpooling research techniques.

Abstract
The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people's lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users' physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.

Abstract
Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.