Criteria of efficiency for conformal prediction

We study optimal conformity measures for various criteria of efficiency of classification in an idealised setting. This leads to an important class of criteria of efficiency that we call probabilistic; it turns out that the most standard criteria of efficiency used in literature on conformal prediction are not probabilistic unless the problem of classification is binary. We consider both unconditional and label-conditional conformal prediction.Comment: 31 page

Suicidality in family caregivers of people with long-term illnesses and disabilities: A scoping review

This is the author accepted manuscript; the final version is available from Elsevier via the DOI in this record.An emerging body of international research suggests family caregivers may be a high-risk group for suicide, but the evidence has not been synthesised. Forty-eight peer-reviewed journal articles were included in this review, spanning low-, middle-, and high-income countries and a variety of illnesses and disabilities. The proportion of caregivers experiencing suicidal ideation ranged from 2.7% to 71%, with evidence of suicide attempts, deaths by suicide, and deaths by homicide-suicide also reported. Risk and protective factors varied across studies and there was little consideration of differences by caregiving relationship, type of illness/disability, or country. There is sufficient evidence to warrant concern for caregivers around the world and prompt action in policy and practice, but more rigorous research is required to draw clear, nuanced conclusions about risk and inform evidence-based prevention and intervention.National Institute for Health Research (NIHR)University of BristolWeston NHS Foundation Trus

Sosyal Sorumluluk Projesi: Liseli Öğrencilerle Yapay Zeka Söyleşileri-NEVŞEHİR Özel Kardelen Fen ve Anadolu Lisesi

When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these confidence values have no theoretical base -- even though the algorithms' predictive performance may be good. There also exist many successful learning algorithms which only depend on the iid assumption. Often however they produce no confidence values for their predictions. Bayesian frameworks are often applied to these algorithms in order to obtain such values, however they can rely on unjustified priors. In this paper we outline the typicalness framework which can be used in conjunction with many other machine learning algorithms. The framework provides confidence information based only on the standard iid assumption and so is much more robust to different underlying data distributions. We show how the framework can be applied to existing algorithms. We also present experimental results which show that the typicalness approach performs close to Bayes when the prior is known to be correct. Unlike Bayes however, the method still gives accurate confidence values even when different data distributions are considered

Mining Tolerance Regions with Model Trees

Many problems encountered in practice involve the prediction of a continuous attribute associated with an example. This problem, known as regression, requires that samples of past experience with known continuous answers are examined and generalized in a regression model to be used in predicting future examples. Regression algorithms deeply investigated in statistics, machine learning and data mining usually lack measures to give an indication of how "good" the predictions are. Tolerance regions, i.e.. a range of possible predictive values, can provide a measure of reliability for every bare prediction. In this paper, we focus on tree-based prediction models, i.e., model trees, and resort to the inductive inference to output tolerance regions in addition to bare prediction. In particular, we consider model trees mined by SMOTI (Stepwise Model Tree Induction) that is a system for data-driven stepwise construction of model trees with regression and splitting nodes and we extend the definition of trees to build tolerance regions to be associated with each leaf. Experiments evaluate validity and quality of output tolerance regions. " Springer-Verlag Berlin Heidelberg 2006

Suicidal thoughts and behaviours in parents caring for children with disabilities and long-term illnesses

Objective: There is a growing body of evidence on suicide risk in family carers, but minimal research on parents caring for children with disabilities and long-term illnesses. The aim of this study was to conduct the first dedicated research on suicide risk in parent carers and identify: (1) the number of parent carers experiencing suicidal thoughts and behaviours, and (2) the risk and protective factors for suicidality in this population. Method: A cross-sectional survey of parent carers in England (n=750), co-produced with parent carers. Suicidal thoughts and behaviours were measured with questions from the Adult Psychiatric Morbidity Survey. Frequencies summarised the proportion of carers experiencing suicidal thoughts and behaviours. Logistic regressions identified risk and protective factors. Results: 42% of parents had experienced suicidal thoughts and behaviours while caring for a disabled or chronically ill child. Only half had sought help for these experiences. Depression, entrapment, dysfunctional coping, and having a mental health diagnosis prior to caring, were significant risk factors. Conclusion: Parent carers contemplate suicide at levels that exceed those of other family carers and the general public. There is an urgent need, in policy and practice, to recognise parent carers as a priority group for prevention and intervention

Local Clustering Conformal Predictor for Imbalanced Data Classification

Part 9: Second Workshop on Conformal Prediction and Its Applications (CoPA 2013)International audienceThe recently developed Conformal Predictor (CP) can provide calibrated confidence for prediction which is out of the traditional predictors’ capacity. However, CP works for balanced data and fails in the case of imbalanced data. To handle this problem, Local Clustering Conformal Predictor (LCCP) which plugs a two-level partition into the framework of CP is proposed. In the first-level partition, the whole imbalanced training dataset is partitioned into some class-taxonomy data subsets. Secondly, the majority class examples proceed to be partitioned into some cluster-taxonomy data subsets by clustering method. To predict a new instance, LCCP selects the nearest cluster, incorporated with the minority class examples, to build a re-balanced training data. The designed LCCP model aims to not only provide valid confidence for prediction, but significantly improve the prediction efficiency as well. The experimental results show that LCCP model presents superiority than CP model for imbalanced data classification

Development of interval soft sensors using enhanced just-in-time learning and inductive confidence predictor

In the development of soft sensors for chemical processes, outliers of input variables and the time-varying nature of the process are difficult to address, thereby often resulting in unavailable prediction. Motivated by these issues, new just-in-time (JIT) learning is derived to track the normal changes of processes regardless of abrupt noises. Such an approach adapts a proposed robust nearest correlation (RNC) algorithm with multimodel ensemble learning to enhance conventional JIT learning. Furthermore, to gauge the quality of the given prediction, we integrate such JIT learning with the inductive confidence predictor (ICP) to yield a new soft sensor called the “interval soft sensor” which generates not only prediction values but also associated confidence values that represent the credibility of a soft sensor’s output. These ideas were applied to a wastewater treatment process. The proposed interval soft sensor was seen to be effective for prediction in the absence and presence of outliers in the process and for validating the online analyzer because of its modeling method independent of output data

Australian democracy and priveleged parliamentary speech

The definitive version is available at www.blackwell-synergy.comThis article responds to recent cases of parliamentary speech which reflect the ascendancy of a totalising ‘mainstream’ approach to public discourse and a political leadership that may, at times, be overly attentive to the majority-rule dimension of democracy. These developments spark a more general discussion of the phenomenology of privileged parliamentary speech, the role of speech freedoms in liberal democratic orders and the duties of parliamentary representatives within them. I make two general conclusions. First, the ways in which we normally argue and think about free speech will not generally apply to the speech of parliamentarians because their speech rights cannot be universalised. Secondly, even if parliamentary speech could be treated as standard speech there would be no legitimate defence (from a liberal democratic point of view) for a strictly populist approach to its use since this could undermine the deliberative function of parliament and lead to the violation of other important liberal democratic principles.Lisa Hil