Semi-supervised learning (SSL) problems are challenging, appear in many domains, and are particularly relevant to streaming applications, where data are abundant but labels are not. The problem tackled here is classification over an evolving data stream where labels are rare and distributed randomly. We propose SLEADE (Stream LEArning by Disagreement Ensemble), a novel method that exploits disagreement-based learning and unsupervised drift detection to leverage unlabeled data during training. SLEADE uses pseudo-labeled instances to augment the training set of each member of an ensemble using amajority trains minorityscheme. The pseudo-labeled data impact is controlled by a weighting function that considers the confidence in the prediction attributed by the ensemble members. SLEADE exploits unsupervised drift detection, which allows the ensemble to respond to changes. We present several experiments using real and synthetic data to illustrate the benefits and limitations of SLEADE compared to existing algorithms.

For many streaming classification tasks, the ground truth labels become available with a non-negligible latency. Given this delayed labelling setting, after the instance data arrives and before its true label is known, the online classifier model may change. Hence, the initial prediction can be replaced with additional periodic predictions gradually produced before the true label becomes available. The quality of these predictions may largely vary. Thus, the question arises of how to summarise the performance of these models when multiple predictions for a single instance are made due to delayed labels.In this study, we aim to provide intuitive performance measures summarising the performance of multiple predictions made for individual instances before their true labels arrive. Particular attention is paid to the fact that under the delayed label setting, the emphasis placed on the quality of initial predictions can vary depending on problem needs. The intermediate performance measures we propose complement existing initial and test-then-train performance evaluation when verification latency is observed. Results provided for both real and synthetic datasets show that the new measures can be used to easily rank methods in terms of their ability to produce and refine predictions before the true labels arrive.

Abstract A large portion of the stream mining studies on classification rely on the availability of true labels immediately after making predictions. This approach is well exemplified by the test-then-train evaluation, where predictions immediately precede true label arrival. However, in many real scenarios, labels arrive with non-negligible latency. This raises the question of how to evaluate classifiers trained in such circumstances. This question is of particular importance when stream mining models are expected to refine their predictions between acquiring instance data and receiving its true label. In this work, we propose a novel evaluation methodology for data streams when verification latency takes place, namely continuous re-evaluation. It is applied to reference data streams and it is used to differentiate between stream mining techniques in terms of their ability to refine predictions based on newly arriving instances. Our study points out, discusses and shows empirically the importance of considering the delay of instance labels when evaluating classifiers for data streams.

In this work, we describe an urban Internet of Things (IoT) architecture, grounded in big data patterns and focused on the needs of cities and their key stakeholders. First, the architecture of the dedicated platform USE4IoT (Urban Service Environment for the Internet of Things), which gathers and processes urban big data and extends the Lambda architecture, is proposed. We describe how the platform was used to make IoT an enabling technology for intelligent transport planning. Moreover, key data processing components vital to provide high-quality IoT data streams in a near-real-time manner are defined. Furthermore, tests showing how the IoT platform described in this study provides a low-latency analytical environment for smart cities are included.

Among different models of neural networks multilayer perceptrons play an important role. Most training methods, including back-propagation concentrate on weight adjustment only. Still the performance of the network strongly depends on its architecture. In our paper the algorithm based on evolutionary programming is proposed. Unlike most other methods of this type, the genotype precision is being evolved together with the architecture and connection weights of the network. Iterative changes in the weight domain make the network structure rough at first so as to tune it later. Not only does it help to avoid inadequate weight precision, but also the search efficiency is increased. Different aspects of the weight set selection are investigated and discussed.