Abstract
Reject option is a technique used to improve classifier's reliability in decision support systems. It consists on withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue have been concerned with implementing a reject option by endowing a supervised learning scheme (e.g., Multilayer Perceptron, Learning Vector Quantization or Support Vector Machines) with a reject mechanism. In this paper we introduce variants of the Self-Organizing Map (SOM), originally an unsupervised learning scheme, to act as supervised classifiers with reject option, and compare their performances with that of the MLP classifier. © 2014 Springer International Publishing Switzerland.

Abstract
Biometric systems based on iris are vulnerable to direct attacks consisting on the presentation of a fake iris to the sensor (a printed or a contact lenses iris image, among others). The mobile biometrics scenario stresses the importance of assessing the security issues. The application of countermeasures against this type of attacking scheme is the problem addressed in the present paper. Widening a previous work, several state-of-the-art iris liveness detection methods were implemented and adapted to a less-constrained scenario. The proposed method combines a feature selection step prior to the use of state-of-the-art classifiers to perform the classification based upon the "best features". Five well known existing databases for iris liveness purposes (Biosec, Clarkson, NotreDame and Warsaw) and a recently published database, MobBIOfake, with real and fake images captured in the mobile scenario were tested. The results obtained suggest that the automated segmentation step does not degrade significantly the results.

Abstract

Abstract

Abstract
The international randomised targeted intraoperative radiotherapy (TARGIT) trial has demonstrated evidence of non-inferiority between the novel technique of TARGIT (intra-operative radiotherapy with Intrabeam(A (R))) and conventional external beam radiotherapy (EBRT) in women with early breast cancer in terms of the primary outcome measure of risk of local relapse within the treated breast. Cosmesis is an increasingly important outcome of breast conserving treatment with both surgery and radiotherapy contributing to this. It was unknown if the single high dose of TARGIT may lead to damaging fibrosis and thus impair cosmesis further, so we objectively evaluated the aesthetic outcome of patients within the TARGIT randomised controlled trial. We have used an objective assessment tool for evaluation of cosmetic outcome. Frontal digital photographs were taken at baseline (before TARGIT or EBRT) and yearly thereafter for up to 5 years. The photographs were analysed by BCCT.core, a validated software which produces a composite score based on symmetry, colour and scar. 342 patients were assessed, median age at baseline 64 years (IQR 59-68). The scores were dichotomised into Excellent and Good (EG), and Fair and Poor (FP). There were statistically significant increases in the odds of having an outcome of EG for patients in the TARGIT group relative to the EBRT group at year 1 (OR 2.07, 95 % CI 1.12-3.85, p = 0.021) and year 2 (OR 2.11, 95 % CI 1.0-4.45, p = 0.05). Following a totally objective assessment in a randomised setting, the aesthetic outcome of patients demonstrates that those treated with TARGIT have a superior cosmetic result to those patients who received conventional external beam radiotherapy.

Abstract
Classification datasets, which feature a skewed class distribution, are said to be class imbalance. Traditional methods favor the larger classes. We propose pairwise ranking as a method for imbalance classification so that learning compares pairs of observations from each class, and therefore both contribute equally to the decision boundary. In previous work, we suggested treating the binary classification as a ranking problem, followed by a threshold mapping to convert back the ranking score to the original classes. In this work, the method is extended to multi-class ordinal classification, and a new mapping threshold is proposed. Results are compared with traditional and ordinal SVMs, and ranking obtains competitive results.