Cost-Sensitive Double Updating Online Learning and Its Application to Online Anomaly Detection

Although both cost-sensitive classification and online learning have been well studied separately in data mining and machine learning, there was very few comprehensive study of cost-sensitive online classification in literature. In this paper, we formally investigate this problem by directly optimizing cost-sensitive measures for an online classification task. As the first comprehensive study, we propose the Cost-Sensitive Double Updating Online Learning (CSDUOL) algorithms, which explores a recent double updating technique to tackle the online optimization task of cost-sensitive classification by maximizing the weighted sum or minimizing the weighted misclassification cost. We theoretically analyze the cost-sensitive measure bounds of the proposed algorithms, extensively examine their empirical performance for cost-sensitive online classification tasks, and finally demonstrate the application of our technique to solve online anomaly detection tasks.

A sequential Bayesian approach to online power quality anomaly segmentation

Increased observability on power distribution networks can reveal signs of incipient faults which can develop into costly and unexpected plant failures. While low-cost sensing and communications infrastructure is facilitating this, it is also highlighting the complex nature of fault signals, a challenge which entails precisely extracting anomalous regions from continuous data streams before classifying the underlying fault signature. Doing this incorrectly will result in capture of uninformative data. Extraction processes can be confounded by operational noise on the network including harmonics produced by embedded generation. In this paper, an online model is proposed. Our Bayesian Changepoint Power Quality anomaly Segmentation allows automated segmentation of anomalies from continuous current waveforms, irrespective of noise. Demonstration of the effectiveness of the proposed technique is carried out with operational field data as well as a challenging simulated network, highlighting the ability to accommodate noise from typical network penetration levels of power electronic devices

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

An adaptive gradient method for online AUC maximization

National Research Foundation (NRF) Singapore under IDM Futures Funding Initiativ

Review on smartphone sensing technology for structural health monitoring

Sensing is a critical and inevitable sector of structural health monitoring (SHM). Recently, smartphone sensing technology has become an emerging, affordable, and effective system for SHM and other engineering fields. This is because a modern smartphone is equipped with various built-in sensors and technologies, especially a triaxial accelerometer, gyroscope, global positioning system, high-resolution cameras, and wireless data communications under the internet-of-things paradigm, which are suitable for vibration- and vision-based SHM applications. This article presents a state-of-the-art review on recent research progress of smartphone-based SHM. Although there are some short reviews on this topic, the major contribution of this article is to exclusively present a compre- hensive survey of recent practices of smartphone sensors to health monitoring of civil structures from the per- spectives of measurement techniques, third-party apps developed in Android and iOS, and various application domains. Findings of this article provide thorough understanding of the main ideas and recent SHM studies on smartphone sensing technology