Segregating Event Streams and Noise with a Markov Renewal Process Model

DS and MP are supported by EPSRC Leadership Fellowship EP/G007144/1

Using Diversity Ensembles with Time Limits to Handle Concept Drift

While traditional supervised learning focuses on static datasets, an increasing amount of data comes in the form of streams, where data is continuous and typically processed only once. A common problem with data streams is that the underlying concept we are trying to learn can be constantly evolving. This concept drift has been of interest to researchers the last few years and there is a need for improved machine learning algorithms that are capable of dealing with concept drifts. A promising approach involves using an ensemble of a diverse set of classifiers. The constituent classifiers are re-trained when a concept drift is detected. Decisions regarding the number of classifiers to maintain and the frequency of re-training classifiers are critical factors that determine classification accuracy in the presence of concept drift. This dissertation systematically investigated these issues in order to develop an improved classifier for online ensemble learning. The impact of reducing the time requiring additional ensembles was studied using artificial and real world datasets. Findings from these studies revealed that in many cases the number of time steps additional ensembles are in memory can be reduced without sacrificing prequential accuracy. It was also found that this new ensemble approach performed well in the presence of false concept drift

Changes in structural network topology correlate with severity of hallucinatory behavior in Parkinson's disease

Inefficient integration between bottom-up visual input and higher order visual processing regions is implicated in visual hallucinations in Parkinson's disease (PD). Here, we investigated white matter contributions to this perceptual imbalance hypothesis. Twenty-nine PD patients were assessed for hallucinatory behavior. Hallucination severity was correlated to connectivity strength of the network using the network-based statistic approach. The results showed that hallucination severity was associated with reduced connectivity within a subnetwork that included the majority of the diverse club. This network showed overall greater between-module scores compared with nodes not associated with hallucination severity. Reduced between-module connectivity in the lateral occipital cortex, insula, and pars orbitalis and decreased within-module connectivity in the prefrontal, somatosensory, and primary visual cortices were associated with hallucination severity. Conversely, hallucination severity was associated with increased between- and within-module connectivity in the orbitofrontal and temporal cortex, as well as regions comprising the dorsal attentional and default mode network. These results suggest that hallucination severity is associated with marked alterations in structural network topology with changes in participation along the perceptual hierarchy. This may result in the inefficient transfer of information that gives rise to hallucinations in PD. Author SummaryInefficient integration of information between external stimuli and internal perceptual predictions may lead to misperceptions or visual hallucinations in Parkinson's disease (PD). In this study, we show that hallucinatory behavior in PD patients is associated with marked alterations in structural network topology. Severity of hallucinatory behavior was associated with decreased connectivity in a large subnetwork that included the majority of the diverse club, nodes with a high number of between-module connections. Furthermore, changes in between-module connectivity were found across brain regions involved in visual processing, top-down prediction centers, and endogenous attention, including the occipital, orbitofrontal, and posterior cingulate cortex. Together, these findings suggest that impaired integration across different sides across different perceptual processing regions may result in inefficient transfer of information

Don’t Pay for Validation: Detecting Drifts from Unlabeled data Using Margin Density

AbstractValidating online stream classifiers has traditionally assumed the availability of labeled samples, which can be monitored over time, to detect concept drift. However, labeling in streaming domains is expensive, time consuming and in certain applications, such as land mine detection, not a possibility at all. In this paper, the Margin Density Drift Detection (MD3) approach is proposed, which can signal change using unlabeled samples and requires labeling only for retraining, in the event of a drift. The MD3 approach when evaluated on 5 synthetic and 5 real world drifting data streams, produced statistically equivalent classification accuracy to that of a fully labeled accuracy tracking drift detector, and required only a third of the samples to be labeled, on average