Dissimilarity-based Ensembles for Multiple Instance Learning

In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation determined by the number of training bags, while the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. In this paper a third, intermediate approach is proposed, which links the two approaches and combines their strengths. Our classifier is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes. We provide guidelines for using such an ensemble, and show state-of-the-art performances on a range of multiple instance learning problems.Comment: Submitted to IEEE Transactions on Neural Networks and Learning Systems, Special Issue on Learning in Non-(geo)metric Space

Implementation of Multiple-Instance Learning in Drug Activity Prediction

In the context of drug discovery and development, much effort has been exerted to determine which conformers of a given molecule are responsible for the observed biological activity. In this work we aimed to predict bioactive conformers using a variant of supervised learning, named multiple-instance learning. A single molecule, treated as a bag of conformers, is biologically active if and only if at least one of its conformers, treated as an instance, is responsible for the observed bioactivity; and a molecule is inactive if none of its conformers is responsible for the observed bioactivity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule. We encoded the 3-dimensional structures using pharmacophore fingerprints which are binary strings, and accomplished instance-based embedding using calculated dissimilarity distances. Four dissimilarity measures were employed and their performances were compared. 1-norm SVM was used for joint feature selection and classification. The approach was applied to four data sets, and the best proposed model for each data set was determined by using the dissimilarity measure yielding the smallest number of selected features. The predictive abilities of the proposed approach were compared with three classical predictive models without instance-based embedding. The proposed approach produced the best predictive models for one data set and second best predictive models for the rest of the data sets, based on the external validations. To validate the ability of the proposed approach to find bioactive conformers, 12 small molecules with co-crystallized structures were seeded in one data set. 10 out of 12 co-crystallized structures were indeed identified as significant conformers using the proposed approach. The proposed approach was demonstrated to be highly competitive with classical predictive models, hence it is very powerful for drug activity prediction. The approach was also validated as a useful method for pursuit of bioactive conformers

Dissimilarity-based multiple instance classification and dictionary learning for bioacoustic signal recognition

In this thesis, two promising and actively researched fields from pattern recognition (PR) and digital signal processing (DSP) are studied, adapted and applied for the automated recognition of bioacoustic signals: (i) learning from weakly-labeled data, and (ii) dictionary-based decomposition. The document begins with an overview of the current methods and techniques applied for the automated recognition of bioacoustic signals, and an analysis of the impact of this technology at global and local scales. This is followed by a detailed description of my research on studying two approaches from the above-mentioned fields, multiple instance learning (MIL) and dictionary learning (DL), as solutions to particular challenges in bioacoustic data analysis. The most relevant contributions and findings of this thesis are the following ones: 1) the proposal of an unsupervised recording segmentation method of audio birdsong recordings that improves species classification with the benefit of an easier implementation since no manual handling of recordings is required; 2) the confirmation that, in the analyzed audio datasets, appropriate dissimilarity measures are those which capture most of the overall differences between bags, such as the modified Hausdorff distance and the mean minimum distance; 3) the adoption of dissimilarity adaptation techniques for the enhancement of dissimilarity-based multiple instance classification, along with the potential further enhancement of the classification performance by building dissimilarity spaces and increasing training set sizes; 4) the proposal of a framework for solving MIL problems by using the one nearest neighbor (1-NN) classifier; 5) a novel convolutive DL method for learning a representative dictionary from a collection of multiple-bird audio recordings; 6) such a DL method is successfully applied to spectrogram denoising and species classification; and, 7) an efficient online version of the DL method that outperforms other state-of-the-art batch and online methods, in both, computational cost and quality of the discovered patternsResumen : En esta tesis se estudian, adaptan y aplican dos prometedoras y activas áreas del reconocimiento de patrones (PR) y procesamiento digital de señales (DSP): (i) aprendizaje débilmente supervisado y (ii) descomposiciones basadas en diccionarios. Inicialmente se hace una revisión de los métodos y técnicas que actualmente se aplican en tareas de reconocimiento automatizado de señales bioacústicas y se describe el impacto de esta tecnología a escalas nacional y global. Posteriormente, la investigación se enfoca en el estudio de dos técnicas de las áreas antes mencionadas, aprendizaje multi-instancia (MIL) y aprendizaje de diccionarios (DL), como soluciones a retos particulares del análisis de datos bioacústicos. Las contribuciones y hallazgos ms relevantes de esta tesis son los siguientes: 1) se propone un método de segmentacin de grabaciones de audio que mejora la clasificación automatizada de especies, el cual es fácil de implementar ya que no necesita información supervisada de entrenamiento; 2) se confirma que, en los conjuntos de datos analizados, las medidas de disimilitudes que capturan las diferencias globales entre bolsas funcionan apropiadamente, tales como la distancia modificada de Hausdorff y la distancia media de los mínimos; 3) la adopción de técnicas de adaptación de disimilitudes para mejorar la clasificación multi-instancia, junto con el incremento potencial del desempeño por medio de la construcción de espacios de disimilitudes y el aumento del tamaño de los conjuntos de entrenamiento; 4) se presenta un esquema para la solución de problemas MIL por medio del clasificador del vecino ms cercano (1-NN); 5) se propone un método novedoso de DL, basado en convoluciones, para el aprendizaje automatizado de un diccionario representativo a partir de un conjunto de grabaciones de audio de múltiples vocalizaciones de aves; 6) dicho mtodo DL se utiliza exitosamente como técnica de reducción de ruido en espectrogramas y clasificación de grabaciones bioacústicas; y 7) un método DL, de procesamiento en línea, que supera otros métodos del estado del arte en costo computacional y calidad de los patrones descubiertosDoctorad

Dissimilarity-based representation for radiomics applications

Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information. Many recent studies have proved that radiomics can offer a lot of useful information that physicians cannot extract from the medical images and can be associated with other information like gene or protein data. However, most of the classification studies in radiomics report the use of feature selection methods without identifying the machine learning challenges behind radiomics. In this paper, we first show that the radiomics problem should be viewed as an high dimensional, low sample size, multi view learning problem, then we compare different solutions proposed in multi view learning for classifying radiomics data. Our experiments, conducted on several real world multi view datasets, show that the intermediate integration methods work significantly better than filter and embedded feature selection methods commonly used in radiomics.Comment: conference, 6 pages, 2 figure

Neural Networks for Complex Data

Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such as graphs and functions. This paper summarizes advances on those themes from the last decade, with a focus on results obtained by members of the SAMM team of Universit\'e Paris