Abstract
This work addresses the problem of mining data streams generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnose degradations of this process, using change detection mechanisms, and self-repair the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learner can detect recurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models. The experimental evaluation on three text mining problems demonstrates the main advantages of the proposed system: it provides information about the recurrence of concepts and rapidly adapts decision models when drift occurs.

Abstract

Abstract
In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models. Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the .

Abstract
Machine learning studies automatic methods for acquisition of domain knowledge with the goal of improving systems performance as the result of experience. In the past two decades, machine learning research and practice has focused on batch learning usually with small data sets. The rationale behind this practice is that examples are generated at random accordingly to some stationary probability distribution. Most learners use a greedy, hill-climbing search in the space of models. They are prone to overfitting, local maximas, etc. Data are scarce and statistic estimates have high variance. A paradigmatic example is the TDIT algorithm to learn decision trees [14]. As the tree grows, less and fewer examples are available to compute the sufficient statistics, variance increase leading to model instability Moreover, the growing process re-uses the same data, exacerbating the overfitting problem. Regularization and pruning mechanisms are mandatory. © Springer International Publishing Switzerland 2015.

Abstract

Abstract