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Gregory Cooper

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7 papers
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7

AAAI Conference 2015 Conference Paper

Obtaining Well Calibrated Probabilities Using Bayesian Binning

  • Mahdi Pakdaman Naeini
  • Gregory Cooper
  • Milos Hauskrecht

Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in artificial intelligence. In this paper we present a new non-parametric calibration method called Bayesian Binning into Quantiles (BBQ) which addresses key limitations of existing calibration methods. The method post processes the output of a binary classification algorithm; thus, it can be readily combined with many existing classification algorithms. The method is computationally tractable, and empirically accurate, as evidenced by the set of experiments reported here on both real and simulated datasets.

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NeurIPS Conference 2005 Conference Paper

A Bayesian Spatial Scan Statistic

  • Daniel Neill
  • Andrew Moore
  • Gregory Cooper

We propose a new Bayesian method for spatial cluster detection, the “Bayesian spatial scan statistic, ” and compare this method to the standard (frequentist) scan statistic approach. We demonstrate that the Bayesian statistic has several advantages over the frequentist approach, including increased power to detect clusters and (since randomization testing is unnecessary) much faster runtime. We evaluate the Bayesian and fre- quentist methods on the task of prospective disease surveillance: detect- ing spatial clusters of disease cases resulting from emerging disease out- breaks. We demonstrate that our Bayesian methods are successful in rapidly detecting outbreaks while keeping number of false positives low.

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JMLR Journal 2005 Journal Article

What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks

  • Weng-Keen Wong
  • Andrew Moore
  • Gregory Cooper
  • Michael Wagner

Traditional biosurveillance algorithms detect disease outbreaks by looking for peaks in a univariate time series of health-care data. Current health-care surveillance data, however, are no longer simply univariate data streams. Instead, a wealth of spatial, temporal, demographic and symptomatic information is available. We present an early disease outbreak detection algorithm called What's Strange About Recent Events (WSARE), which uses a multivariate approach to improve its timeliness of detection. WSARE employs a rule-based technique that compares recent health-care data against data from a baseline distribution and finds subgroups of the recent data whose proportions have changed the most from the baseline data. In addition, health-care data also pose difficulties for surveillance algorithms because of inherent temporal trends such as seasonal effects and day of week variations. WSARE approaches this problem using a Bayesian network to produce a baseline distribution that accounts for these temporal trends. The algorithm itself incorporates a wide range of ideas, including association rules, Bayesian networks, hypothesis testing and permutation tests to produce a detection algorithm that is careful to evaluate the significance of the alarms that it raises. [abs] [ pdf ][ bib ] &copy JMLR 2005. ( edit, beta )

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NeurIPS Conference 2004 Conference Paper

Instance-Specific Bayesian Model Averaging for Classification

  • Shyam Visweswaran
  • Gregory Cooper

Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for learning instance-specific models from data that are optimized to predict well for a particular instance. Based on this framework, we present a that performs selective model averaging over a restricted class of Bayesian networks. On experimental evaluation, this algorithm shows superior performance over model selection. We intend to apply such instance-specific algorithms to improve the performance of patient-specific predictive models induced from medical data. instance-specific algorithm called ISA

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NeurIPS Conference 1996 Conference Paper

Learning Bayesian Belief Networks with Neural Network Estimators

  • Stefano Monti
  • Gregory Cooper

In this paper we propose a method for learning Bayesian belief networks from data. The method uses artificial neural networks as probability estimators, thus avoiding the need for making prior assumptions on the nature of the probability distributions govern(cid: 173) ing the relationships among the participating variables. This new method has the potential for being applied to domains containing both discrete and continuous variables arbitrarily distributed. We compare the learning performance of this new method with the performance of the method proposed by Cooper and Herskovits in [7]. The experimental results show that, although the learning scheme based on the use of ANN estimators is slower, the learning accuracy of the two methods is comparable. Category: Algorithms and Architectures.

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