Dynamic Bayesian Combination of Multiple Imperfect Classifiers

Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications. In many situations, such as when human decisions need to be combined, the base decisions can vary enormously in reliability. A Bayesian approach to such uncertain combination allows us to infer the differences in performance between individuals and to incorporate any available prior knowledge about their abilities when training data is sparse. In this paper we explore Bayesian classifier combination, using the computationally efficient framework of variational Bayesian inference. We apply the approach to real data from a large citizen science project, Galaxy Zoo Supernovae, and show that our method far outperforms other established approaches to imperfect decision combination. We go on to analyse the putative community structure of the decision makers, based on their inferred decision making strategies, and show that natural groupings are formed. Finally we present a dynamic Bayesian classifier combination approach and investigate the changes in base classifier performance over time.Comment: 35 pages, 12 figure

Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems

Crowdsourcing systems commonly face the problem of aggregating multiple judgments provided by potentially unreliable workers. In addition, several aspects of the design of efficient crowdsourcing processes, such as defining worker's bonuses, fair prices and time limits of the tasks, involve knowledge of the likely duration of the task at hand. Bringing this together, in this work we introduce a new time--sensitive Bayesian aggregation method that simultaneously estimates a task's duration and obtains reliable aggregations of crowdsourced judgments. Our method, called BCCTime, builds on the key insight that the time taken by a worker to perform a task is an important indicator of the likely quality of the produced judgment. To capture this, BCCTime uses latent variables to represent the uncertainty about the workers' completion time, the tasks' duration and the workers' accuracy. To relate the quality of a judgment to the time a worker spends on a task, our model assumes that each task is completed within a latent time window within which all workers with a propensity to genuinely attempt the labelling task (i.e., no spammers) are expected to submit their judgments. In contrast, workers with a lower propensity to valid labeling, such as spammers, bots or lazy labelers, are assumed to perform tasks considerably faster or slower than the time required by normal workers. Specifically, we use efficient message-passing Bayesian inference to learn approximate posterior probabilities of (i) the confusion matrix of each worker, (ii) the propensity to valid labeling of each worker, (iii) the unbiased duration of each task and (iv) the true label of each task. Using two real-world public datasets for entity linking tasks, we show that BCCTime produces up to 11% more accurate classifications and up to 100% more informative estimates of a task's duration compared to state-of-the-art methods

BCCNet: Bayesian classifier combination neural network

Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO partners to ensure the machine learning algorithms address appropriate user needs whether for tactical decision making or evidence-based policy decisions. In one particular case, we developed and deployed a novel algorithm, BCCNet, to quickly process large quantities of unstructured data to prevent and respond to natural disasters. Crowdsourcing provides an efficient mechanism to generate labels from unstructured data to prime machine learning algorithms for large scale data analysis. However, these labels are often imperfect with qualities varying among different citizen scientists, which prohibits their direct use with many state-of-the-art machine learning techniques. We describe BCCNet, a framework that simultaneously aggregates biased and contradictory labels from the crowd and trains an automatic classifier to process new data. Our case studies, mosquito sound detection for malaria prevention and damage detection for disaster response, show the efficacy of our method in the challenging context of developing world applications.Comment: Presented at NeurIPS 2018 Workshop on Machine Learning for the Developing Worl

Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity.

A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints on the spatio-temporal continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results in an annotation that is significantly more accurate than what would be obtained by frame-by-frame evaluation of the classifier output. The framework has been implemented and applied successfully to the analysis of team sports with a single camera. Key words: Visua

Towards Automated Machine Learning on Imperfect Data for Situational Awareness in Power System

The increasing penetration of renewable energy sources (such as solar and wind) and incoming widespread electric vehicles charging introduce new challenges in the power system. Due to the variability and uncertainty of these sources, reliable and cost-effective operations of the power system rely on high level of situational awareness. Thanks to the wide deployment of sensors (e.g., phasor measurement units (PMUs) and smart meters) and the emerging smart Internet of Things (IoT) sensing devices in the electric grid, large amounts of data are being collected, which provide golden opportunities to achieve high level of situational awareness for reliable and cost-effective grid operations.To better utilize the data, this dissertation aims to develop Machine Learning (ML) methods and provide fundamental understanding and systematic exploitation of ML for situational awareness using large amounts of imperfect data collected in power systems, in order to improve the reliability and resilience of power systems.However, building excellent ML models needs clean, accurate and sufficient training data. The data collected from the real-world power system is of low quality. For example, the data collected from wind farms contains a mixture of ramp and non-ramp as well as the mingle of heterogeneous dynamics data; the data in the transmission grid contains noisy, missing, insufficient and inaccurate timestamp data. Employing ML without considering these distinct features in real-world applications cannot build good ML models. This dissertation aims to address these challenges in two applications, wind generation forecast and power system event classification, by developing ML models in an automated way with less efforts from domain experts, as the cost of processing such large amounts of imperfect data by experts can be prohibitive in practice.First, we take heterogeneous dynamics into consideration, especially for ramp events. A Drifting Streaming Peaks-over-Threshold (DSPOT) enhanced self-evolving neural networks-based short-term wind farm generation forecast is proposed by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events, based on which different neural networks are trained to learn different dynamics of wind farm generation. As the efficacy of the neural networks relies on the quality of training datasets (i.e., the classification accuracy of ramp and non-ramp events), a Bayesian optimization based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. Next, we address the challenges of event classification due to the low-quality PMU measurements and event logs. A novel machine learning framework is proposed for robust event classification, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Moreover, with the small number of good features, we need much less training data to train a good event classifier, which can address the challenge of insufficient and imbalanced training data, and the training time is negligible compared to neural network based approaches. Based on the proposed framework, we developed a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data. Subsequently, we address the challenge of insufficient training labels. The real-world PMU data is often incomplete and noisy, which can significantly reduce the efficacy of existing machine learning techniques that require high-quality labeled training data. To obtain high-quality event logs for large amounts of PMU measurements, it requires significant efforts from domain experts to maintain the event logs and even hand-label the events, which can be prohibitively costly or impractical in practice. So we develop a weakly supervised machine learning approach that can learn a good event classifier using a few labeled PMU data. The key idea is to learn the labels from unlabeled data using a probabilistic generative model, in order to improve the training of the event classifiers. Experimental results show that even with 95\% of unlabeled data, the average accuracy of the proposed method can still achieve 78.4\%. This provides a promising way for domain experts to maintain the event logs in a less expensive and automated manner. Finally, we conclude the dissertation and discuss future directions

Using spatio-temporal continuity constraints to enhance visual tracking of moving objects

We present a framework for annotating dynamic scenes involving occlusion and other uncertainties. Our system comprises an object tracker, an object classifier and an algorithm for reasoning about spatio-temporal continuity. The principle behind the object tracking and classifier modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine resolves error, ambiguity and occlusion to produce a most likely hypothesis, which is consistent with global spatio-temporal continuity constraints. The system results in improved annotation over frame-by-frame methods. It has been implemented and applied to the analysis of a team sports video