Who Said What: Modeling Individual Labelers Improves Classification

Data are often labeled by many different experts with each expert only labeling a small fraction of the data and each data point being labeled by several experts. This reduces the workload on individual experts and also gives a better estimate of the unobserved ground truth. When experts disagree, the standard approaches are to treat the majority opinion as the correct label or to model the correct label as a distribution. These approaches, however, do not make any use of potentially valuable information about which expert produced which label. To make use of this extra information, we propose modeling the experts individually and then learning averaging weights for combining them, possibly in sample-specific ways. This allows us to give more weight to more reliable experts and take advantage of the unique strengths of individual experts at classifying certain types of data. Here we show that our approach leads to improvements in computer-aided diagnosis of diabetic retinopathy. We also show that our method performs better than competing algorithms by Welinder and Perona (2010), and by Mnih and Hinton (2012). Our work offers an innovative approach for dealing with the myriad real-world settings that use expert opinions to define labels for training.Comment: AAAI 201

Some considerations on the WHO Histological classification of laryngeal neoplasms

A new edition of the World Health Organization (WHO) Histological classification of tumours of the hypopharynx, larynx, trachea and parapharyngeal space was published in 2017. We have considered this classification regarding laryngeal neoplasms and discuss the grounds for said revision. Many of the laryngeal neoplasms described in the literature and in the previous WHO edition from 2005 have been omitted from this current revision. Many are described elsewhere in the book but it may give the new generation of pathologists/surgeons/oncologists the false impression that these tumour entities do not exist in the larynx.info:eu-repo/semantics/publishedVersio

Impact of Prenatal Checkups of Mothers and Immunization of Children on the Health Status of Children (0-3 years) - A Study in Rural areas of Aligarh District, Uttar Pradesh.

Background and objectives: A survey based study on rural areas of Aligarh District was conducted to assess the prenatal checkups pregnant women and its effects on health status of children between the age of (0-3 years), and immunization received by children and its effects on their health status. Methods: Five hundred children from five villages of rural areas of Aligarh District were randomly selected. For the purpose of the study, a self prepared structured interview schedule was used. To get the qualitative information of the study anthropometric measures include height weight were used for assessing growth pattern of the child. The stepwise analysis of two variables height for age and weight for age was done on the basis of Water low’s and Gomez’ classification. To examine the relationship between Health Status of the child and selected variable that affects Health Status of children, Chi-square test was employed. Results: Based on Gomez’ classification out of 88% mothers who did not go for prenatal ups majority 80% of children were underweight, and 80% of children who did not receive immunization majority 60% were underweight. Based on Waterlow’s classification majority 68% of children were stunted whose mother did not go for prenatal checkups and 50% of their children were stunted who did not receive immunization. Conclusion: Majority of children were stunted whose mother did not go for prenatal checkup and the children who did not receive immunization

Enhancing Sensitivity Classification with Semantic Features using Word Embeddings

Government documents must be reviewed to identify any sensitive information they may contain, before they can be released to the public. However, traditional paper-based sensitivity review processes are not practical for reviewing born-digital documents. Therefore, there is a timely need for automatic sensitivity classification techniques, to assist the digital sensitivity review process. However, sensitivity is typically a product of the relations between combinations of terms, such as who said what about whom, therefore, automatic sensitivity classification is a difficult task. Vector representations of terms, such as word embeddings, have been shown to be effective at encoding latent term features that preserve semantic relations between terms, which can also be beneficial to sensitivity classification. In this work, we present a thorough evaluation of the effectiveness of semantic word embedding features, along with term and grammatical features, for sensitivity classification. On a test collection of government documents containing real sensitivities, we show that extending text classification with semantic features and additional term n-grams results in significant improvements in classification effectiveness, correctly classifying 9.99% more sensitive documents compared to the text classification baseline

Toric anti-self-dual Einstein metrics via complex geometry

Using the twistor correspondence, we give a classification of toric anti-self-dual Einstein metrics: each such metric is essentially determined by an odd holomorphic function. This explains how the Einstein metrics fit into the classification of general toric anti-self-dual metrics given in an earlier paper (math.DG/0602423). The results complement the work of Calderbank-Pedersen (math.DG/0105263), who describe where the Einstein metrics appear amongst the Joyce spaces, leading to a different classification. Taking the twistor transform of our result gives a new proof of their theorem.Comment: v2. Published version. Additional references. 14 page

Comparative analysis of text classification algorithms for automated labelling of quranic verses

The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. We applied three text classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as “Shahadah” (the first pillar of Islam) or “Pray” (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses

Two steps forward, one step back? A commentary on the disease-specific core sets of the international classification of functioning, Disability and Health (ICF)

The International Classification of Functioning, Disability and Health (ICF) is advocated as a biopsychosocial framework and classification and has been received favourably by occupational therapists, disability rights organisations and proponents of the social model of disability. The success of the ICF largely depends on its uptake in practice and it is considered unwieldy in its full format. Therefore, to make the ICF user friendly, the World Health Organisation (WHO) have condensed the original format and developed core sets, some of which are disease specific. The authors use the ICF Core Set for stroke as an example to debate if by reverting to classification according to disease, the ICF is at risk of taking two steps forward, one step back in its holistic portrayal of health