Abstract
Ordinal classification is a form of multiclass classification for which there is an inherent order between the classes, but not a meaningful numeric differerence between them. The performance of such classifiers is usually assessed by measures appropriate for nominal classes or for regression. Unfortunately, these do not account for the true dimension of the error. The goal of this work is to show that existing measures for evaluating ordinal classification models surffer from a number of important shortcomings. For this reason, we propose an alternative measure defined directly in the confusion matrix. An error coefficient appropriate for ordinal data should capture how much the result diverges from the ideal prediction and how "inconsistent" the classifier is in regard to the relative order of the classes. The proposed coefficient results from the observation that the performance yielded by the Misclassification Error Rate coefficient is the benefit of the path along the diagonal of the confusion matrix. We carry out an experimental study which confirms the usefulness of the novel metric.

Abstract
Although Optical Music Recognition (OMR) has been the focus of much research for decades, the processing of handwritten musical scores is not yet satisfactory. The efforts made to find robust symbol representations and learning methodologies have not found a similar quality in the learning of the dissimilarity concept. Simple Euclidean distances are often used to measure dissimilarity between different examples. However, such distances do not necessarily yield the best performance. In this paper, we propose to learn the best distance for the k-nearest neighbor (k-NN) classifier. The distance concept will be tuned both for the application domain and the adopted representation for the music symbols. The performance of the method is compared with the support vector machine (SVM) classifier using both real and synthetic music scores. The synthetic database includes four types of deformations inducing variability in the printed musical symbols which exist in handwritten music sheets. The work presented here can open new research paths towards a novel automatic musical symbols recognition module for handwritten scores. © 2011 IEEE.

Abstract
In the predictive modeling tasks, a clear distinction is often made between learning problems that are supervised or unsupervised, the first involving only labeled data (training patterns with known category labels) while the latter involving only unlabeled data. There is a growing interest in a hybrid setting, called semi-supervised learning, in semi-supervised classification, the labels of only a small portion of the training data set are available. The unlabeled data, instead of being discarded, are also used in the learning process. Motivated by a breast cancer application, in this work we address a new learning task, in-between classification and semi-supervised classification. Each example is described using two different feature sets, not necessarily both observed for a given example. If a single view is observed, then the class is only due to that feature set, if both views are present the observed class label is the maximum of the two values corresponding to the individual views. We propose new learning methodologies adapted to this learning paradigm and experimentally compare them with baseline methods from the conventional supervised and unsupervised settings. The experimental results verify the usefulness of the proposed approaches. © 2011 IEEE.

Abstract

Abstract
Feature selection is a topic of growing interest mainly due to the increasing amount of information, being an essential task in many machine learning problems with high dimensional data. The selection of a subset of relevant features help to reduce the complexity of the problem and the building of robust learning models. This work presents an adaptation of a recent quadratic programming feature selection technique that identifies in one-fold the redundancy and relevance on data. Our approach introduces a non-probabilistic measure to capture the relevance based on Minimum Spanning Trees. Three different real datasets were used to assess the performance of the adaptation. The results are encouraging and reflect the utility of feature selection algorithms.

Abstract
This paper describes an approach based on the shortest path method for the detection and tracking of vibrating lines. The detection and tracking of vibrating structures, such as lines and cables, is of great importance in areas such as civil engineering, but the specificities of these scenarios make it a hard problem to tackle. We propose a two-step approach consisting of line detection and subsequent tracking. The automatic detection of the lines avoids manual initialization - a typical problem of these scenarios - and favors tracking. The additional information provided by the line detection enables the improvement of existing algorithms and extends their application to a larger set of scenarios.