Feature Relevance Bounds for Ordinal Regression

The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model accuracy, the interpretation of these models itself is of high relevance, and existing approaches therefore enforce e.g. model sparsity. For high dimensional or highly correlated data, however, this might be misleading due to strong variable dependencies. In this contribution, we aim for an identification of feature relevance bounds which - besides identifying all relevant features - explicitly differentiates between strongly and weakly relevant features

A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Information

There is growing interest in affective computing for the representation and prediction of emotions along ordinal scales. However, the term ordinal emotion label has been used to refer to both absolute notions such as low or high arousal, as well as relation notions such as arousal is higher at one instance compared to another. In this paper, we introduce the terminology absolute and relative ordinal labels to make this distinction clear and investigate both with a view to integrate them and exploit their complementary nature. We propose a Markovian framework referred to as Dynamic Ordinal Markov Model (DOMM) that makes use of both absolute and relative ordinal information, to improve speech based ordinal emotion prediction. Finally, the proposed framework is validated on two speech corpora commonly used in affective computing, the RECOLA and the IEMOCAP databases, across a range of system configurations. The results consistently indicate that integrating relative ordinal information improves absolute ordinal emotion prediction.Comment: This work has been submitted to IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Statistical Methods to Enhance Clinical Prediction with High-Dimensional Data and Ordinal Response

Der technologische Fortschritt ermöglicht es heute, die moleculare Konfiguration einzelner Zellen oder ganzer Gewebeproben zu untersuchen. Solche in großen Mengen produzierten hochdimensionalen Omics-Daten aus der Molekularbiologie lassen sich zu immer niedrigeren Kosten erzeugen und werden so immer häufiger auch in klinischen Fragestellungen eingesetzt. Personalisierte Diagnose oder auch die Vorhersage eines Behandlungserfolges auf der Basis solcher Hochdurchsatzdaten stellen eine moderne Anwendung von Techniken aus dem maschinellen Lernen dar. In der Praxis werden klinische Parameter, wie etwa der Gesundheitszustand oder die Nebenwirkungen einer Therapie, häufig auf einer ordinalen Skala erhoben (beispielsweise gut, normal, schlecht). Es ist verbreitet, Klassifikationsproblme mit ordinal skaliertem Endpunkt wie generelle Mehrklassenproblme zu behandeln und somit die Information, die in der Ordnung zwischen den Klassen enthalten ist, zu ignorieren. Allerdings kann das Vernachlässigen dieser Information zu einer verminderten Klassifikationsgüte führen oder sogar eine ungünstige ungeordnete Klassifikation erzeugen. Klassische Ansätze, einen ordinal skalierten Endpunkt direkt zu modellieren, wie beispielsweise mit einem kumulativen Linkmodell, lassen sich typischerweise nicht auf hochdimensionale Daten anwenden. Wir präsentieren in dieser Arbeit hierarchical twoing (hi2) als einen Algorithmus für die Klassifikation hochdimensionler Daten in ordinal Skalierte Kategorien. hi2 nutzt die Mächtigkeit der sehr gut verstandenen binären Klassifikation, um auch in ordinale Kategorien zu klassifizieren. Eine Opensource-Implementierung von hi2 ist online verfügbar. In einer Vergleichsstudie zur Klassifikation von echten wie von simulierten Daten mit ordinalem Endpunkt produzieren etablierte Methoden, die speziell für geordnete Kategorien entworfen wurden, nicht generell bessere Ergebnisse als state-of-the-art nicht-ordinale Klassifikatoren. Die Fähigkeit eines Algorithmus, mit hochdimensionalen Daten umzugehen, dominiert die Klassifikationsleisting. Wir zeigen, dass unser Algorithmus hi2 konsistent gute Ergebnisse erzielt und in vielen Fällen besser abschneidet als die anderen Methoden

1 Exploitation of Pairwise Class Distances for Ordinal Classification

model, latent variable Ordinal classification refers to classification problems in which the classes have a natural order imposed on them because of the nature of the concept studied. Some ordinal classification approaches perform a projection from the input space to 1-dimensional(latent) space that is partitioned into a sequence of intervals (one for each class). Class identity of a novel input pattern is then decided based on the interval its projection falls into. This projection is trained only indirectly as part of the overall model fitting. As with any latent model fitting, direct construction hints one may have about the desired form of the latent model can prove very useful for obtaining high quality models. The key idea of this paper is to construct such a projection model directly, using insights about the class distribution obtained from pairwise distance calculations. The proposed approach is extensively evaluated with eight nominal and ordinal classifiers methods, ten real world ordinal classification datasets, and four different performance measures. The new methodology obtained the best results in average ranking when considering three of the performance metrics, although significant differences are found only for some of the methods. Also, after observing other methods internal behaviour in the latent space, we conclude that the internal projection do not fully reflect the intra-class behaviour of the patterns. Our method is intrinsically simple, intuitive and easily understandable, yet, highly competitive with state-of-the-art approaches to ordinal classification.

Datation automatique de photographies à partir de caractéristiques textuelles et visuelles

In this thesis, we address the problem of automatic dating of old photographs using attributes from imageand surrounding text.Over the last centuries, photography and metadata have undergone many mutations. In an informativeand contextualizing vocation, we report our observations regarding these technological advances. Thisinformation helps to understand the evolution of photographs dating methods that have historically beenstudied manually before becoming recently automatic.We focused in a fundamental concept of dating : the ordinal nature of time. To this extent we studied ordinalclassification methods and associated evaluation measures.In this thesis, we have built a large collection of web pages combining old photographs and text content. Thiscollection aims at allowing both training and evaluation of the proposed models. This represents an advancein the field of photographs dating because no comparable data set previously existed.We propose an original model that we have developed and evaluated on image databases to date thephotographs with their visual content. The major feature of our approach is the exploitation of the ordinalstructure of time. This is to our knowledge the first use of this intrinsic characteristic of photographs dating.Moreover, we explore the dating of photographs by the use of the surrounding text data. In our approach,we mimic the human behavior used in the context of photographs dating. Thus we propose for the first timean automatic method for the dating of photographs by the use of the textual environment.Dans cette thèse, la problématique que nous traitons est celle de la datation automatique de photographiesanciennes à partir d'attributs issus de l'image et du texte qui l'entoure.Au cours de ces derniers siècles, la photographie et les métadonnées ont subi de nombreuses mutations. Dans une vocation informative et contextualisante, nous reportons nos observations au regard de ces avancées technologiques qui permettent de comprendre l'évolution des méthodes de datation de photographies, historiquement manuelles et récemment devenues automatiques.Nous nous sommes intéressés à une notion fondamentale et intrinsèque de la datation : le caractère ordinal du temps. Nous avons donc étudié les méthodes de classification ordinale et les mesures d'évaluation associées.Pour cette thèse, nous avons construit une large collection de pages web associant clichés anciens et contenus textuels. Celle-ci a pour vocation de permettre l'entraînement et l'évaluation des modèles proposés. Ceci représente une avancée dans ce domaine car aucun jeu de données comparable n'existait auparavant.Nous proposons un modèle original que nous avons développé et évalué sur des bases de photographies pour les dater grâce à leur contenu visuel. La particularité majeure de notre approche est l'exploitation pour la première fois du caractère ordinal du temps.Enfin, nous explorons la datation de photographies par l'usage des données textuelles environnantes. Notre démarche imite le comportement humain utilisé lors de la datation de photographies en contexte. Ainsi nous proposons pour la première fois une méthode automatique permettant de dater des clichés par l'usage de l'environnement textuel

An Ordinal Approach to Affective Computing

Both depression prediction and emotion recognition systems are often based on ordinal ground truth due to subjectively annotated datasets. Yet, both have so far been posed as classification or regression problems. These naive approaches have fundamental issues because they are not focused on ordering, unlike ordinal regression, which is the most appropriate for truly ordinal ground truth. Ordinal regression to date offers comparatively fewer, more limited methods when compared with other branches in machine learning, and its usage has been limited to specific research domains. Accordingly, this thesis presents investigations into ordinal approaches for affective computing by describing a consistent framework to understand all ordinal system designs, proposing ordinal systems for large datasets, and introducing tools and principles to select suitable system designs and evaluation methods. First, three learning approaches are compared using the support vector framework to establish the empirical advantages of ordinal regression, which is lacking from the current literature. Results on depression and emotion corpora indicate that ordinal regression with proper tuning can improve existing depression and emotion systems. Ordinal logistic regression (OLR), which is an extension of logistic regression for ordinal scales, contributes to a number of model structures, from which the best structure must be chosen. Exploiting the newly proposed computationally efficient greedy algorithm for model structure selection (GREP), OLR outperformed or was comparable with state-of-the-art depression systems on two benchmark depression speech datasets. Deep learning has dominated many affective computing fields, and hence ordinal deep learning is an attractive prospect. However, it is under-studied even in the machine learning literature, which motivates an in-depth analysis of appropriate network architectures and loss functions. One of the significant outcomes of this analysis is the introduction of RankCNet, a novel ordinal network which utilises a surrogate loss function of rank correlation. Not only the modelling algorithm but the choice of evaluation measure depends on the nature of the ground truth. Rank correlation measures, which are sensitive to ordering, are more apt for ordinal problems than common classification or regression measures that ignore ordering information. Although rank-based evaluation for ordinal problems is not new, so far in affective computing, ordinality of the ground truth has been widely ignored during evaluation. Hence, a systematic analysis in the affective computing context is presented, to provide clarity and encourage careful choice of evaluation measures. Another contribution is a neural network framework with a novel multi-term loss function to assess the ordinality of ordinally-annotated datasets, which can guide the selection of suitable learning and evaluation methods. Experiments on multiple synthetic and affective speech datasets reveal that the proposed system can offer reliable and meaningful predictions about the ordinality of a given dataset. Overall, the novel contributions and findings presented in this thesis not only improve prediction accuracy but also encourage future research towards ordinal affective computing: a different paradigm, but often the most appropriate