Abstract
Operations Research (OR) and Artificial Intelligence (AI) disciplines have been playing major roles on the design of new intelligent systems. Recently, different contributions from both fields have been made on the models design for problems with multi-criteria. The credit scoring problem is an example of that. In this problem, one evaluates how unlikely a client will default with his payments. Client profiles are evaluated, being their results expressed in terms of an ordinal score scale (Excelent Good Fair Poor). Intelligent systems have then to take in consideration different criteria such as payment history, mortgages, wages among others in order to commit their outcome. To achieve this goal, researchers have been delving models capable to render these multiple criteria encompassed on ordinal data. The literature presents a myriad of different methods either on OR or AI fields for the multi-criteria models. However, a description of ordinal data methods on these two major disciplines and their relations has not been thoroughly conducted yet. It is key for further research to identify the developments made and the present state of the existing methods. It is also important to ascertain current achievements and what the requirements are to attain intelligent systems capable to capture relationships from data. In this chapter one will describe techniques presented for over more than five decades on OR and AI disciplines applied to multi-criteria ordinal problems.

Abstract
IntroductionBreast cosmesis is an important endpoint of breast conserving therapy (BCT), but a gold standard method of its evaluation is not yet established. The St. George and Wollongong Randomised Breast Boost trial used five different methods of cosmetic assessment, including both subjective and objective, to comprehensively evaluate the cosmetic outcome of the trial patients. This current study analyses the level of concordance between these methods in an attempt to determine a possible standard in the evaluation of breast cosmesis. MethodsPatients attending follow-up clinic reviews at 5years post breast radiotherapy were evaluated. Patients completed a cosmesis and functional assessment questionnaire, assessing clinicians completed an EORTC (European Organization for Research and Treatment of Cancer) cosmetic rating questionnaire and photographs were obtained. The photographs were later assessed by a panel of five experts, as well as analysed using the objective pBRA (relative Breast Retraction Assessment) and the BCCT.core (Breast Cancer Conservative Treatment.cosmetic results) computer software. Scores were dichotomised to excellent/good and fair/poor. Pairwise comparisons between all methods, except pBRA, were carried out using overall agreement calculations and kappa scores. pBRA scores were compared on a continuous scale with each of the other dichotomised scores obtained by the other four methods. ResultsOf 513 St George patients alive at 5years, 385 (75%) attended St George for follow-up and consented to photography. Results showed that assessment by physicians in clinic and patient self-assessment were more favourable regarding overall cosmetic outcome than evaluation of photographs by the panel or the BCCT.core software. Excellent/good scores by clinician-live and patient self-assessments were 93% and 94% respectively (agreement 89%), as compared to 75% and 74% only by BCCT.core and panel assessments respectively (agreement 83%, kappa 0.57). For the pBRA measurements, there was a statistically significant difference (P <0.001) between scores for excellent/good versus fair/poor cosmesis by all four methods. The range of median pBRA measurements for fair/poor scores was 13.4-14.8 and for excellent/good scores was 8.0-9.4. ConclusionIncorporating both BCCT.core assessment and patient self-assessment could potentially provide the basis of a gold standard method of breast cosmetic evaluation. BCCT.core represents an easy, time efficient, reproducible, cost effective and reliable method, however, it lacks the functional and psychosocial elements of cosmesis that only patient self-reported outcomes can provide.

Abstract
Nowadays imbalanced learning represents one of the most vividly discussed challenges in machine learning. In these scenarios, one or some of the classes in the problem have a significantly lower a priori probability, usually leading to trivial or non-desirable classifiers. Because of this, imbalanced learning has been researched to a great extent by means of different approaches. Recently, the focus has switched from binary classification to other paradigms where imbalanced data also arise, such as ordinal classification. This paper tests the application of learning pairwise ranking with multiple granularity levels in an ordinal and imbalanced classification problem where the aim is to construct an accurate model for donor-recipient allocation in liver transplantation. Our experiments show that approaching the problem as ranking solves the imbalance issue and leads to a competitive performance.

Abstract
Sign Language Recognition (SLR) has becoming one of the most important research areas in the field of human computer interaction. SLR systems are meant to automatically translate sign language into text or speech, in order to reduce the communicational gap between deaf and hearing people. The aim of this paper is to exploit multimodal learning techniques for an accurate SLR, making use of data provided by Kinect and Leap Motion. In this regard, single-modality approaches as well as different multimodal methods, mainly based on convolutional neural networks, are proposed. Experimental results demonstrate that multimodal learning yields an overall improvement in the sign recognition performance.

Abstract

Abstract
In this paper we introduce a new conceptualization for the reduction of the number of support vectors (SVs) for an efficient design of support vector machines. The techniques here presented provide a good balance between SVs reduction and generalization capability. Our proposal explores concepts from classification with reject option. These methods output a third class (the rejected instances) for a binary problem when a prediction cannot be given with sufficient confidence. Rejected instances along with misclassified ones are discarded from the original data to give rise to a classification problem that can be linearly solved. Our experimental study on two benchmark datasets show significant gains in terms of SVs reduction with competitive performances.