Boosting through Optimization of Margin Distributions

Boosting has attracted much research attention in the past decade. The success of boosting algorithms may be interpreted in terms of the margin theory. Recently it has been shown that generalization error of classifiers can be obtained by explicitly taking the margin distribution of the training data into account. Most of the current boosting algorithms in practice usually optimizes a convex loss function and do not make use of the margin distribution. In this work we design a new boosting algorithm, termed margin-distribution boosting (MDBoost), which directly maximizes the average margin and minimizes the margin variance simultaneously. This way the margin distribution is optimized. A totally-corrective optimization algorithm based on column generation is proposed to implement MDBoost. Experiments on UCI datasets show that MDBoost outperforms AdaBoost and LPBoost in most cases.Comment: 9 pages. To publish/Published in IEEE Transactions on Neural Networks, 21(7), July 201

Postgenomics: Proteomics and Bioinformatics in Cancer Research

Now that the human genome is completed, the characterization of the proteins encoded by the sequence remains a challenging task. The study of the complete protein complement of the genome, the “proteome,” referred to as proteomics, will be essential if new therapeutic drugs and new disease biomarkers for early diagnosis are to be developed. Research efforts are already underway to develop the technology necessary to compare the specific protein profiles of diseased versus nondiseased states. These technologies provide a wealth of information and rapidly generate large quantities of data. Processing the large amounts of data will lead to useful predictive mathematical descriptions of biological systems which will permit rapid identification of novel therapeutic targets and identification of metabolic disorders. Here, we present an overview of the current status and future research approaches in defining the cancer cell's proteome in combination with different bioinformatics and computational biology tools toward a better understanding of health and disease

Totally corrective boosting algorithm and application to face recognition

Boosting is one of the most well-known learning methods for building highly accurate classifiers or regressors from a set of weak classifiers. Much effort has been devoted to the understanding of boosting algorithms. However, questions remain unclear about the success of boosting. In this thesis, we study boosting algorithms from a new perspective. We started our research by empirically comparing the LPBoost and AdaBoost algorithms. The result and the corresponding analysis show that, besides the minimum margin, which is directly and globally optimized in LPBoost, the margin distribution plays a more important role. Inspired by this observation, we theoretically prove that the Lagrange dual problems of AdaBoost, LogitBoost and soft-margin LPBoost with generalized hinge loss are all entropy maximization problems. By looking at the dual problems of these boosting algorithms, we show that the success of boosting algorithms can be understood in terms of maintaining a better margin distribution by maximizing margins and at the same time controlling the margin variance. We further point out that AdaBoost approximately maximizes the average margin, instead of the minimum margin. The duality formulation also enables us to develop column-generation based optimization algorithms, which are totally corrective. The new algorithm, which is termed AdaBoost-CG, exhibits almost identical classification results to those of standard stage-wise additive boosting algorithms, but with much faster convergence rates. Therefore, fewer weak classifiers are needed to build the ensemble using our proposed optimization technique. The significance of margin distribution motivates us to design a new column-generation based algorithm that directly maximizes the average margin while minimizes the margin variance at the same time. We term this novel method MDBoost and show its superiority over other boosting-like algorithms. Moreover, consideration of the primal and dual problems together leads to important new insights into the characteristics of boosting algorithms. We then propose a general framework that can be used to design new boosting algorithms. A wide variety of machine learning problems essentially minimize a regularized risk functional. We show that the proposed boosting framework, termed AnyBoostTc, can accommodate various loss functions and different regularizers in a totally corrective optimization way. A large body of totally corrective boosting algorithms can actually be solved very efficiently, and no sophisticated convex optimization solvers are needed, by solving the primal rather than the dual. We also demonstrate that some boosting algorithms like AdaBoost can be interpreted in our framework, even their optimization is not totally corrective, . We conclude our study by applying the totally corrective boosting algorithm to a long-standing computer vision problem-face recognition. Linear regression face recognizers, constrained by two categories of locality, are selected and combined within both the traditional and totally corrective boosting framework. To our knowledge, it is the first time that linear-representation classifiers are boosted for face recognition. The instance-based weak classifiers bring some advantages, which are theoretically or empirically proved in our work. Benefiting from the robust weak learner and the advanced learning framework, our algorithms achieve the best reported recognition rates on face recognition benchmark datasets

Apprentissage automatique pour la détection de relations d'affaire

Les documents publiés par des entreprises, tels les communiqués de presse, contiennent une foule d’informations sur diverses activités des entreprises. C’est une source précieuse pour des analyses en intelligence d’affaire. Cependant, il est nécessaire de développer des outils pour permettre d’exploiter cette source automatiquement, étant donné son grand volume. Ce mémoire décrit un travail qui s’inscrit dans un volet d’intelligence d’affaire, à savoir la détection de relations d’affaire entre les entreprises décrites dans des communiqués de presse. Dans ce mémoire, nous proposons une approche basée sur la classification. Les méthodes de classifications existantes ne nous permettent pas d’obtenir une performance satisfaisante. Ceci est notamment dû à deux problèmes : la représentation du texte par tous les mots, qui n’aide pas nécessairement à spécifier une relation d’affaire, et le déséquilibre entre les classes. Pour traiter le premier problème, nous proposons une approche de représentation basée sur des mots pivots c’est-à-dire les noms d’entreprises concernées, afin de mieux cerner des mots susceptibles de les décrire. Pour le deuxième problème, nous proposons une classification à deux étapes. Cette méthode s’avère plus appropriée que les méthodes traditionnelles de ré-échantillonnage. Nous avons testé nos approches sur une collection de communiqués de presse dans le domaine automobile. Nos expérimentations montrent que les approches proposées peuvent améliorer la performance de classification. Notamment, la représentation du document basée sur les mots pivots nous permet de mieux centrer sur les mots utiles pour la détection de relations d’affaire. La classification en deux étapes apporte une solution efficace au problème de déséquilibre entre les classes. Ce travail montre que la détection automatique des relations d’affaire est une tâche faisable. Le résultat de cette détection pourrait être utilisé dans une analyse d’intelligence d’affaire.Documents published by companies such as press releases, contain a wealth of information on various business activities. This is a valuable source for business intelligence analysis; but automatic tools are needed to exploit such large volume data. The work described in this thesis is part of a research project on business intelligence, namely we aim at the detection of business relationships between companies described in press releases. In this thesis, we consider business relation detection as a problem of classification. However, the existing classification methods do not allow us to obtain a satisfactory performance. This is mainly due to two problems: the representation of text using all the content words, which do not necessarily a business relationship; and the imbalance between classes. To address the first problem, we propose representations based on words that are between or close to the names of companies involved (which we call pivot words) in order to focus on words having a higher chance to describe a relation. For the second problem, we propose a two-stage classification. This method is more effective than the traditional resampling methods. We tested our approach on a collection of press releases in the automotive industry. Our experiments show that both proposed approaches can improve the classification performance. They perform much better than the traditional feature selection methods and the resampling method. This work shows the feasibility of automatic detection of business relations. The result of this detection could be used in an analysis of business intelligence