ROC Optimisation of Safety Related Systems

1st Workshop on ROC Analysis in Artificial Intelligence (ROCAI 2004), part of the 16th European Conference on Artificial Intelligence, Valencia, Spain, 22-27 August 2004Many safety related and critical systems warn of potentially dangerous events; for example the Short Term Conflict Alert (STCA) system warns of airspace infractions between aircraft. Although installed with current technology such critical systems may become out of date due to changes in the circumstances in which they function, operational procedures and the regulatory environment. Current practice is to ‘tune’ by hand the many parameters governing the system in order to optimise the operating point in terms of the true positive and false positive rates, which are frequently associated with highly imbalanced costs. In this paper we cast the tuning of critical systems as a multiobjective optimisation problem. We show how a region of the optimal receiver operating characteristic (ROC) curve may be obtained, permitting the system operators to select the operating point. We apply this methodology to the STCA system, using a multi-objective (1 + 1)-evolution strategy, showing that we can improve upon the current hand-tuned operating point as well as providing the salient ROC curve describing the true-positive versus false-positive tradeoff

Multi-objective optimisation of safety related systems: An application to Short Term Conflict Alert.

Copyright © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Notes: In this paper multi-objective optimisation is used for the first time to adjust the 1500 parameters of Short-Term Conflict Alert systems to optimise the Receiver Operating Characteristic (ROC) by simultaneously reducing the false positive rate and increasing the true positive alert rate, something that previous work by other researchers had not succeeded in doing. Importantly for such safety-critical systems, the method also yields an assessment of the confidence that may be placed in the optimised ROC curves. The paper results from a collaboration with NATS and a current KTP project, also with NATS, is deploying the methods in air-traffic control centres nationwide.Many safety related and critical systems warn of potentially dangerous events; for example, the short term conflict alert (STCA) system warns of airspace infractions between aircraft. Although installed with current technology, such critical systems may become out of date due to changes in the circumstances in which they function, operational procedures, and the regulatory environment. Current practice is to "tune," by hand, the many parameters governing the system in order to optimize the operating point in terms of the true positive and false positive rates, which are frequently associated with highly imbalanced costs. We cast the tuning of critical systems as a multiobjective optimization problem. We show how a region of the optimal receiver operating characteristic (ROC) curve may be obtained, permitting the system operators to select the operating point. We apply this methodology to the STCA system, using a multiobjective (1+1) evolution strategy, showing that we can improve upon the current hand-tuned operating point, as well as providing the salient ROC curve describing the true positive versus false positive tradeoff. We also provide results for three-objective optimization of the alert response time in addition to the true and false positive rates. Additionally, we illustrate the use of bootstrapping for representing evaluation uncertainty on estimated Pareto fronts, where the evaluation of a system is based upon a finite set of representative data

Assessment and optimisation of STCA performance: Using the Pareto optimal receiver operating characteristic

EUROCONTROL Annual Safety R&D Seminar (2008), Southampton, UK, 22-24 October 2008Short Term Conflict Alert (STCA) systems are complex software programs, with many parameters that must be adjusted to achieve best performance. We describe a simple evolutionary algorithm for optimising the trade-off between wanted alerts and nuisance alerts. The procedure yields an estimate of the Pareto optimal Receiver Operating Characteristic for the STCA system and we discuss additional uses of this for characterising and comparing the performance of STCA systems and airspaces

Multi-objective optimisation for receiver operating characteristic analysis

Copyright © 2006 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Multi-Objective Machine LearningSummary Receiver operating characteristic (ROC) analysis is now a standard tool for the comparison of binary classifiers and the selection operating parameters when the costs of misclassification are unknown. This chapter outlines the use of evolutionary multi-objective optimisation techniques for ROC analysis, in both its traditional binary classification setting, and in the novel multi-class ROC situation. Methods for comparing classifier performance in the multi-class case, based on an analogue of the Gini coefficient, are described, which leads to a natural method of selecting the classifier operating point. Illustrations are given concerning synthetic data and an application to Short Term Conflict Alert

Variable interactions and exploring parameter space in an expensive optimisation problem: Optimising Short Term Conflict Alert

Copyright © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.2010 IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain, 18-23 July 2010Short Term Conflict Alert (STCA) systems provide warnings to air traffic controllers if aircraft are in danger of becoming too close. They are complex software programs, with many inter-dependent parameters that must be adjusted to achieve the best trade-off between wanted and nuisance alerts. We describe a multi-archive evolutionary algorithm for optimising regional parameter subsets in parallel, reducing the number of evaluations required to generate an estimated Pareto optimal Receiver Operating Characteristic (ROC), showing that it provides superior results to traditional single-archived algorithms. A method of `aggressive' optimisation, designed to explore unknown parameter ranges in a `safe' manner, is shown to yield more extensive and better converged estimated Pareto fronts

A multi-modal event detection system for river and coastal marine monitoring applications

Abstract—This work is investigating the use of a multi-modal sensor network where visual sensors such as cameras and satellite imagers, along with context information can be used to complement and enhance the usefulness of a traditional in-situ sensor network in measuring and tracking some feature of a river or coastal location. This paper focuses on our work in relation to the use of an off the shelf camera as part of a multi-modal sensor network for monitoring a river environment. It outlines our results in relation to the estimation of water level using a visual sensor. It also outlines the benefits of a multi-modal sensor network for marine environmental monitoring and how this can lead to a smarter, more efficient sensing network

Formulation and comparison of multi-class ROC surfaces

2nd ROCML workshop, held within the 22nd International Conference on Machine Learning (ICML 2005), Bonn, Germany, 7-11 August 2005The Receiver Operating Characteristic (ROC) has become a standard tool for the analysis and comparison of classifiers when the costs of misclassification are unknown. There has been relatively little work, however, examining ROC for more than two classes. Here we define the ROC surface for the Q-class problem in terms of a multi-objective optimisation problem in which the goal is to simultaneously minimise the Q(Q − 1) mis-classification rates, when the misclassification costs and parameters governing the classifier’s behaviour are unknown. We present an evolutionary algorithm to locate the optimal trade-off surface between misclassifications of different types. The performance of the evolutionary algorithm is illustrated on a synthetic three class problem. In addition the use of the Pareto optimal surface to compare classifiers is discussed, and we present a straightforward multi-class analogue of the Gini coefficient. This is illustrated on synthetic and standard machine learning dat

Multi-class ROC analysis from a multi-objective optimisation perspective

Copyright © 2006 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition Letters . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, Vol. 27 Issue 8 (2006), DOI: 10.1016/j.patrec.2005.10.016Notes: Receiver operating characteristics (ROC) are traditionally used for assessing and tuning classifiers discriminating between two classes. This paper is the first to set ROC analysis in a multi-objective optimisation framework and thus generalise ROC curves to any number of classes, showing how multi-objective optimisation may be used to optimise classifier performance. An important new result is that the appropriate measure for assessing overall classifier quality is the Gini coefficient, rather than the volume under the ROC surface as previously thought. The method is currently being exploited in a KTP project with AI Corporation on detecting credit card fraud.The receiver operating characteristic (ROC) has become a standard tool for the analysis and comparison of classifiers when the costs of misclassification are unknown. There has been relatively little work, however, examining ROC for more than two classes. Here we discuss and present an extension to the standard two-class ROC for multi-class problems. We define the ROC surface for the Q-class problem in terms of a multi-objective optimisation problem in which the goal is to simultaneously minimise the Q(Q − 1) misclassification rates, when the misclassification costs and parameters governing the classifier’s behaviour are unknown. We present an evolutionary algorithm to locate the Pareto front—the optimal trade-off surface between misclassifications of different types. The use of the Pareto optimal surface to compare classifiers is discussed and we present a straightforward multi-class analogue of the Gini coefficient. The performance of the evolutionary algorithm is illustrated on a synthetic three class problem, for both k-nearest neighbour and multi-layer perceptron classifiers