The management of context-sensitive features: A review of strategies

In this paper, we review five heuristic strategies for handling context- sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning researchers fits into this framework. While we do not claim that these strategies exhaust the possibilities, it appears that the framework includes all of the techniques that can be found in the published literature on context-sensitive learning

A context-aware approach for handling concept drift in classification

Adapting classification models to changes is one of the main challenges associated with learning from data in dynamic environments. In particular, the description of the target concept is not static and may change over time under the influence of varying environmental conditions (i.e. varying context). Although many adaptive learning approaches have been proposed in the literature to address such changes, these are limited in terms of the extent to which the contextual aspects are explicitly identified and utilised. Instead, existing approaches mostly rely on monitoring the effects of drift (in terms of the degradation of the classifier’s performance). Given this, to achieve more effective concept drift management, we propose incorporating context awareness when adapting the classification model to changes. Explicit identification and monitoring of the contextual aspects enable capturing the causes of drift, and hence facilitating more proactive adaptation. In particular, we propose an information-theoretic-based approach for systematic context identification, aiming to learn from data the contextual characteristics of the domain of interest by identifying the context variables contributing to concept changes. Such characteristics are then utilised as important clues guiding the adaptation process of the classification model. Specifically, knowledge of contextual variables are exploited to select the most relevant data for retraining the model via a data weighting model, and to signal the need for data re-selection via a change detection model. The experimental analyses on simulated, benchmark, and real-world datasets, show that such explicit identification and utilisation of contextual information result in a more effective data selection and drift detection strategies, and enable to produce more accurate predictions

A learning perspective on individual differences in skilled reading: Exploring and exploiting orthographic and semantic discrimination cues

The goal of the present study is to understand the role orthographic and semantic information play in the behaviour of skilled readers. Reading latencies from a self-paced sentence reading experiment in which Russian near-synonymous verbs were manipulated appear well-predicted by a combination of bottom-up sub-lexical letter triplets (trigraphs) and top-down semantic generalizations, modelled using the Naive Discrimination Learner. The results reveal a complex interplay of bottom-up and top-down support from orthography and semantics to the target verbs, whereby activations from orthography only are modulated by individual differences. Using performance on a serial reaction time task for a novel operationalization of the mental speed hypothesis, we explain the observed individual differences in reading behaviour in terms of the exploration/exploitation hypothesis from Reinforcement Learning, where initially slower and more variable behaviour leads to better performance overall

Context Exploitation in Data Fusion

Complex and dynamic environments constitute a challenge for existing tracking algorithms. For this reason, modern solutions are trying to utilize any available information which could help to constrain, improve or explain the measurements. So called Context Information (CI) is understood as information that surrounds an element of interest, whose knowledge may help understanding the (estimated) situation and also in reacting to that situation. However, context discovery and exploitation are still largely unexplored research topics. Until now, the context has been extensively exploited as a parameter in system and measurement models which led to the development of numerous approaches for the linear or non-linear constrained estimation and target tracking. More specifically, the spatial or static context is the most common source of the ambient information, i.e. features, utilized for recursive enhancement of the state variables either in the prediction or the measurement update of the filters. In the case of multiple model estimators, context can not only be related to the state but also to a certain mode of the filter. Common practice for multiple model scenarios is to represent states and context as a joint distribution of Gaussian mixtures. These approaches are commonly referred as the join tracking and classification. Alternatively, the usefulness of context was also demonstrated in aiding the measurement data association. Process of formulating a hypothesis, which assigns a particular measurement to the track, is traditionally governed by the empirical knowledge of the noise characteristics of sensors and operating environment, i.e. probability of detection, false alarm, clutter noise, which can be further enhanced by conditioning on context. We believe that interactions between the environment and the object could be classified into actions, activities and intents, and formed into structured graphs with contextual links translated into arcs. By learning the environment model we will be able to make prediction on the target\u2019s future actions based on its past observation. Probability of target future action could be utilized in the fusion process to adjust tracker confidence on measurements. By incorporating contextual knowledge of the environment, in the form of a likelihood function, in the filter measurement update step, we have been able to reduce uncertainties of the tracking solution and improve the consistency of the track. The promising results demonstrate that the fusion of CI brings a significant performance improvement in comparison to the regular tracking approaches