Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

The challenge of cancer control in Africa.

While the world is focused on controlling the spread of diseases such as HIV and malaria in the developing world, another approaching epidemic has been largely overlooked. The World Heath Organization predicts that there will be 16 million new cancer cases per year in 2020 and 70% of these will be in the developing world. Many of these cancers are preventable, or treatable when detected early enough. Establishing effective, affordable and workable cancer control plans in African countries is one step in the right direction toward limiting this epidemic.

Design, construction and evaluation of systems to predict risk in obstetrics.

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Earthquakes, influenza and cycles of Indian kala-azar.

It is suggested that previous data indicate 3 major epidemics of kala-azar in Assam between 1875 and 1950, with inter-epidemic periods of 30-45 and 20 years. This deviates from the popular view of regular cycles with a 10-20 year period. A deterministic mathematical model of kala-azar is used to find the simplest explanation for the timing of the 3 epidemics, paying particular attention to the role of extrinsic (drugs, natural disasters, other infectious diseases) versus intrinsic (host and vector dynamics, birth and death rates, immunity) processes in provoking the second. We conclude that, whilst widespread influenza in 1918-1919 may have magnified the second epidemic, intrinsic population processes provide the simplest explanation for its timing and synchrony throughout Assam. The model also shows that the second inter-epidemic period is expected to be shorter than the first, even in the absence of extrinsic agents, and highlights the importance of a small fraction of patients becoming chronically infectious (with post kala-azar dermal leishmaniasis) after treatment during an epidemic.

N-best error simulation for training spoken dialogue systems

A recent trend in spoken dialogue research is the use of reinforcement learning to train dialogue systems in a simulated environment. Past researchers have shown that the types of errors that are simulated can have a significant effect on simulated dialogue performance. Since modern systems typically receive an N-best list of possible user utterances, it is important to be able to simulate a full N-best list of hypotheses. This paper presents a new method for simulating such errors based on logistic regression, as well as a new method for simulating the structure of N-best lists of semantics and their probabilities, based on the Dirichlet distribution. Off-line evaluations show that the new Dirichlet model results in a much closer match to the receiver operating characteristics (ROC) of the live data. Experiments also show that the logistic model gives confusions that are closer to the type of confusions observed in live situations. The hope is that these new error models will be able to improve the resulting performance of trained dialogue systems. © 2012 IEEE.