Using clustering to aid text classification of single-labelled datasets

Supervised and unsupervised learning have been the focus of critical research in the areas of machine learning and artificial intelligence. In the literature, these two streams flow independently of each other, despite their close conceptual and practical connections. This dissertation demonstrates that unsupervised learning algorithms, i.e. clustering, can provide us with valuable information about the data and help in the creation of high-accuracy text classifiers. In the case of clustering,the aim is to extract a kind of \structure" from a given sample of objects. The reasoning behind this is that if some structure exists in the objects, it is possible to take advantage of this information and find a short description of the data,exploiting the dependence or association between index terms and documents.This concise representation of the whole dataset can be properly incorporated in the existing data representation. The use of prior knowledge about the nature oft he dataset helps in building a more efficient classifier for this set. This approach does not capture all the intricacies of text; however on some domains this technique substantially improves text classification accuracy.In this vein, a study of the interaction between supervised and unsupervised learning has been carried out. We have studied and implemented models that apply clustering in multiple ways and in conjunction with classification to construct robust text classifiers. The extensive experimentation has shown the effectiveness of using clustering to boost text classification performance. Additionally, preliminary experiments on some of the most important applications of text classification such as Spam Mail Filtering, Spam Detection in Social Bookmarking Systems,and Sentence Boundary Disambiguation, have shown promising enhancements by exploiting the proposed models

Using clustering to enhance text classification

This paper addresses the problem of learning to classify texts by exploiting information derived from clustering both train-ing and testing sets. The incorporation of knowledge result-ing from clustering into the feature space representation of the texts is expected to boost the performance of a classi-fier. Experiments conducted on several widely used datasets demonstrate the effectiveness of the proposed algorithm es-pecially for small training sets

Text Classification Using Clustering

This paper addresses the problem of learning to classify texts by exploiting information derived from both training and testing sets. To accomplish this, clustering is used as a complementary step to text classification, and is applied not only to the training set but also to the testing set. This approach allows us to estimate the location of the testing examples and the structure of the whole dataset, which is not possible for an inductive learner. The incorporation of the knowledge resulting from clustering to the simple BOW representation of the texts is expected to boost the performance of a classifier. Experiments conducted on tasks and datasets provided in the framework of the ECDL/PKDD 2006 Challenge Discovery on personalized spam filtering, demonstrate the e#ectiveness of the proposed approach. The experiments show substantial improvements on classification performance especially for small training sets

A Framework for a Knowledge Management

Managing the educational content and knowledge is one of the greatest challenges for e-learning environments. This work is a proposition of a system for capturing and managing individual learners' and learning communities' knowledge that arises from the creation of educational electronic portfolios. This paper describes preliminary research regarding the design and development of an innovative tool that will offer solutions and a new perspective to the field of digital portfolios. Its purpose is to become a mean for the creation of educational portfolios, aiming to support learners and tutors in personal and community basis, with exploiting in a clever way the information attained and turning it into knowledge

Critically ill cancer patient in intensive care unit: Issues that arise

Advances in the management of malignancies and organ failures have led to substantial increases in survival as well as in the number of cancer patients requiring intensive care unit (ICU) admission. Although effectiveness of ICU in this group remains controversial, the heterogeneity of its population in terms of the nature and curability of their disease and the severity of critical illness and underlying conditions may explain the plethora of issues arising when considering cancer patients for ICU admission, especially from the view of limited resources and ICU beds. The most frequent reasons leading a cancer patient to ICU are postoperative, respiratory failure, infection, and sepsis. Although reasons of admission, nature and number of organ failures, type of malignancy, and therapies that have preceded ICU admission may affect outcome, reliable scoring systems or survival predictors are missing. Literature suggests that organ dysfunction should be managed at its onset, whereas aggressive ICU management should be reappraised after a few days of full support. A multidisciplinary treating team of physicians should aid in changing the goals from restorative to palliative care when there appears to be no possible benefit from any treatment. End-of life-decisions and code status should be made by consensus, based on patients’ autonomy and dignity. Further interventional multicenter studies are required to assess post-ICU burden, long-term medical outcomes, and quality of life in this cohort of patients. (C) 2014 Elsevier Inc. All rights reserved

Immune Response to Mycobacterial Infection: Lessons from Flow Cytometry

Detecting and treating active and latent tuberculosis are pivotal elements for effective infection control; yet, due to their significant inherent limitations, the diagnostic means for these two stages of tuberculosis (TB) to date remain suboptimal. This paper reviews the current diagnostic tools for mycobacterial infection and focuses on the application of flow cytometry as a promising method for rapid and reliable diagnosis of mycobacterial infection as well as discrimination between active and latent TB: it summarizes diagnostic biomarkers distinguishing the two states of infection and also features of the distinct immune response against Mycobacterium tuberculosis (Mtb) at certain stages of infection as revealed by flow cytometry to date

Factor XIII deficiency as a potential cause of supratentorial haemorrhage after posterior fossa surgery

Postoperative intracranial haemorrhage can be a dramatic event, carrying significant morbidity and mortality. Bleeding at sites remote from the operation area represents a small percentage of haemorrhages whose aetiology remains unclear (Harders et al. Acta Neurochir (Wien) 74(1-2):57-60, 1985). We present the case of a 60-year-old patient who underwent posterior fossa craniotomy for the removal of a space-occupying lesion and suffered supratentorial haemorrhage soon after the operation. A thorough postoperative investigation revealed low levels of factor XIII (FXIII), the factor mainly responsible for fibrin clot stabilisation. We suggest that reduced FXIII activity may be an important but preventable predisposing factor to remote postoperative haemorrhage in neurosurgical patients

Factor XIII deficiency as a potential cause of supratentorial haemorrhage after posterior fossa surgery

BACKGROUND: Postoperative intracranial haemorrhage can be a dramatic event, carrying significant morbidity and mortality. Bleeding at sites remote from the operation area represents a small percentage of haemorrhages whose aetiology remains unclear (Harders et al. Acta Neurochir (Wien) 74(1-2):57-60, 1985). AIM: We present the case of a 60-year-old patient who underwent posterior fossa craniotomy for the removal of a space-occupying lesion and suffered supratentorial haemorrhage soon after the operation. RESULTS: A thorough postoperative investigation revealed low levels of factor XIII (FXIII), the factor mainly responsible for fibrin clot stabilisation. CONCLUSION: We suggest that reduced FXIII activity may be an important but preventable predisposing factor to remote postoperative haemorrhage in neurosurgical patients