Tracking recurrent concepts using context

The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts. In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost

Mining recurring concepts in a dynamic feature space

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS

Collaborative data stream mining in ubiquitous environments using dynamic classifier selection

In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets

Mars: a personalised mobile activity recognition system

Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on today’s smart phones. The state of the art in mobile activity recognition uses traditional classification learning techniques. Thus, the learning process typically involves: i) collection of labelled sensory data that is transferred and collated in a centralised repository; ii) model building where the classification model is trained and tested using the collected data; iii) a model deployment stage where the learnt model is deployed on-board a mobile device for identifying activities based on new sensory data. In this paper, we demonstrate the Mobile Activity Recognition System (MARS) where for the first time the model is built and continuously updated on-board the mobile device itself using data stream mining. The advantages of the on-board approach are that it allows model personalisation and increased privacy as the data is not sent to any external site. Furthermore, when the user or its activity profile changes MARS enables promptly adaptation. MARS has been implemented on the Android platform to demonstrate that it can achieve accurate mobile activity recognition. Moreover, we can show in practise that MARS quickly adapts to user profile changes while at the same time being scalable and efficient in terms of consumption of the device resources

Learning Recurring Concepts from Data Streams in Ubiquitous Environments

Due to recent scientific and technological advances in information systems it is now possible to continuously record data at high speeds in a wide range of devices. The need to make sense of such massive amounts of data opens an opportunity to create new data stream classification techniques to model and predict the behavior of streaming data. When learning from data streams, the problem of concept drift means that the underlying data distributions can change over time. This has a strong impact on classification techniques, as predictive models become invalid and have to be updated. Furthermore, these changes in concept are usually a consequence of changes in context, and this relationship could be exploited to handle concept drift. Recurring concepts is a particular case of concept drift, where concepts that have drifted can suddenly reoccur. In this situation it may be possible to avoid relearning these previously observed concepts. However, the few existing approaches that take advantage of concept recurrence are neither designed to take context into consideration nor to take into account the resources required to store representations of past concepts. Both issues are of particular significance for ubiquitous data stream mining, where the learning process is executed in dynamically changing environments using resource constrained devices. Moreover, most existing techniques assume that the underlying data stream feature space is static. However, in many real-world applications the set of features and their relevance to the target concept may change over time. Despite its importance, this issue has received little attention, particularly on how it can be eficiently addressed when tracking recurring concepts. Sharing knowledge among ubiquitous devices to collaboratively improve the modeling of local concepts is another interesting idea which has not been properly explored. This could improve the accuracy of the local model as it would benefit from patterns similar to the local concept that were observed in other ubiquitous devices, but not yet locally. In addition, the deployment of data stream classification as an autonomous and adaptive service to support the data analysis requirements of ubiquitous applications is still an open issue that lacks research in the field of ubiquitous data stream mining. This PhD thesis addresses the aforementioned open issues, focusing on learning anytime, anywhere classification models from data streams in ubiquitous environments, where the underlying concepts may change over time, with special emphasis on recurring concepts. Four main contributions are presented: _ The MReC (Mining Recurring Concepts) approach that integrates context with previously learned concepts to improve the adaptation to recurring concepts. Moreover, to deal with situations of resource constraints, an intelligent strategy to discard models is also proposed. _ The MReC-DFS (Mining Recurring Concepts in a Dynamic Feature Space) approach, that extends MReC to address the challenges of a dynamic feature space while simultaneously reducing the memory cost of storing past models. In addition, a novel incremental feature selection method is proposed that dynamically determines the threshold used to select the most relevant features for a certain concept. _ A Collaborative Data Stream Mining (Coll-Stream) approach that explores the knowledge available in the community to improve local classification accuracy. Coll-Stream integrates community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the instance space. _ A UDSM (Ubiquitous Data Stream Mining) Service to support the data analysis requirements of ubiquitous applications. As the basis for our service we describe a general mechanism, which autonomously adapts the execution of the data stream classification process to each situation, using context and resource awareness. Finally, the experimental validation of the proposed contributions using synthetic and real datasets allows us to achieve the objectives and answer the research questions proposed for this dissertation