Abstract
While ordinal classification problems are common in many situations, induction of ordinal decision trees has not evolved significantly. Conventional trees for regression settings or nominal classification are commonly induced for ordinal classification problems. On the other hand a decision tree consistent with the ordinal setting is often desirable to aid decision making in such situations as credit rating. In this work we extend a recently proposed strategy based on constraints defined globally over the feature space. We propose a bootstrap technique to improve the accuracy of the baseline solution. Experiments in synthetic and real data show the benefits of our proposal. © 2011 IEEE.

Abstract
This is a conceptual paper supported by empirical research giving details of a new Business Narrative Modelling Language (BNML). The need for BNML arose given a growing dissatisfaction with qualitative research approaches and also due to the need to bring entrepreneurs, especially those with little training in management theory, closer to the academic (as well as practitioner) discussion of innovation and strategy for value creation. We aim primarily for an improved communication process of events which can be described using the narrative, in the discussion of the value creation process. Our findings, illustrated through a case study, should be of interest to both researchers and practitioners alike.

Abstract
The goal of sound morphing by feature interpolation is to obtain sounds whose values of features are intermediate between those of the source and target sounds. In order to do this, we should be able to resynthesize sounds that present a set of predefined feature values, a notoriously difficult problem. In this work, we present morphing techniques to obtain hybrid musical instrument sounds whose feature values correspond as close as possible to the ideal interpolated values. When the features capture perceptually relevant information, the morphed sound whose features are interpolated is perceptually intermediate. The features we use are acoustic correlates of salient timbre dimensions derived from perceptual studies, such that sounds whose feature values are intermediate between two would be placed between them in the underlying timbre space. We measure the perceptual impact of the morphed sounds directly by the feature values, using them as an objective measure with which to evaluate the results. Thus we consider that the morphed sounds change perceptually linearly when the corresponding feature values vary linearly.

Abstract
Global markets impose strong requirements to manufacturing domain in terms of flexibility, robustness and reconfigurability. The multi-agent systems (MAS) paradigm is suitable to handle such requirements, introducing an alternative way to design complex, agile and adaptive systems. However, MAS based solutions may suffer of myopia due to the local optimal decision-making performed by the autonomous distributed agents having a partial knowledge of the problem. This paper depicts the optimization problem in MAS, particularly having in mind the achievement of adaptation, and explores the contributions that biology can offer to handle this issue. Two bio-inspired MAS solutions for routing pallets in a real assembly system are described to illustrate how optimization and adaptation can be combined.

Abstract
[No abstract available]

Abstract
In this work we show that combining physician rules and machine learned rules may improve the performance of a classifier that predicts whether a breast cancer is missed on percutaneous, image-guided breast core needle biopsy (subsequently referred to as "breast core biopsy"). Specifically, we show how advice in the form of logical rules, derived by a sub-specialty, i.e. fellowship trained breast radiologists (subsequently referred to as "our physicians") can guide the search in an inductive logic programming system, and improve the performance of a learned classifier. Our dataset of 890 consecutive benign breast core biopsy results along with corresponding mammographic findings contains 94 cases that were deemed non-definitive by a multidisciplinary panel of physicians, from which 15 were upgraded to malignant disease at surgery. Our goal is to predict upgrade prospectively and avoid surgery in women who do not have breast cancer. Our results, some of which trended toward significance, show evidence that inductive logic programming may produce better results for this task than traditional propositional algorithms with default parameters. Moreover, we show that adding knowledge from our physicians into the learning process may improve the performance of the learned classifier trained only on data.