Nonlinear Boosting Projections for Ensemble Construction

In this paper we propose a novel approach for ensemble construction based on the use of nonlinear projections to achieve both accuracy and diversity of individual classifiers. The proposed approach combines the philosophy of boosting, putting more effort on difficult instances, with the basis of the random subspace method. Our main contribution is that instead of using a random subspace, we construct a projection taking into account the instances which have posed most difficulties to previous classifiers. In this way, consecutive nonlinear projections are created by a neural network trained using only incorrectly classified instances. The feature subspace induced by the hidden layer of this network is used as the input space to a new classifier. The method is compared with bagging and boosting techniques, showing an improved performance on a large set of 44 problems from the UCI Machine Learning Repository. An additional study showed that the proposed approach is less sensitive to noise in the data than boosting method

Estudio de métodos de construcción de ensembles de clasificadores y aplicaciones

La inteligencia artificial se dedica a la creación de sistemas informáticos con un comportamiento inteligente. Dentro de este área el aprendizaje computacional estudia la creación de sistemas que aprenden por sí mismos. Un tipo de aprendizaje computacional es el aprendizaje supervisado, en el cual, se le proporcionan al sistema tanto las entradas como la salida esperada y el sistema aprende a partir de estos datos. Un sistema de este tipo se denomina clasificador. En ocasiones ocurre, que en el conjunto de ejemplos que utiliza el sistema para aprender, el número de ejemplos de un tipo es mucho mayor que el número de ejemplos de otro tipo. Cuando esto ocurre se habla de conjuntos desequilibrados. La combinación de varios clasificadores es lo que se denomina "ensemble", y a menudo ofrece mejores resultados que cualquiera de los miembros que lo forman. Una de las claves para el buen funcionamiento de los ensembles es la diversidad. Esta tesis, se centra en el desarrollo de nuevos algoritmos de construcción de ensembles, centrados en técnicas de incremento de la diversidad y en los problemas desequilibrados. Adicionalmente, se aplican estas técnicas a la solución de varias problemas industriales.Ministerio de Economía y Competitividad, proyecto TIN-2011-2404

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes

RandomBoost: Simplified Multi-class Boosting through Randomization

We propose a novel boosting approach to multi-class classification problems, in which multiple classes are distinguished by a set of random projection matrices in essence. The approach uses random projections to alleviate the proliferation of binary classifiers typically required to perform multi-class classification. The result is a multi-class classifier with a single vector-valued parameter, irrespective of the number of classes involved. Two variants of this approach are proposed. The first method randomly projects the original data into new spaces, while the second method randomly projects the outputs of learned weak classifiers. These methods are not only conceptually simple but also effective and easy to implement. A series of experiments on synthetic, machine learning and visual recognition data sets demonstrate that our proposed methods compare favorably to existing multi-class boosting algorithms in terms of both the convergence rate and classification accuracy.Comment: 15 page

Ensemble learning of high dimension datasets

Ensemble learning, an approach in Machine Learning, makes decisions based on the collective decision of a committee of learners to solve complex tasks with minimal human intervention. Advances in computing technology have enabled researchers build datasets with the number of features in the order of thousands and enabled building more accurate predictive models. Unfortunately, high dimensional datasets are especially challenging for machine learning due to the phenomenon dubbed as the "curse of dimensionality". One approach to overcoming this challenge is ensemble learning using Random Subspace (RS) method, which has been shown to perform very well empirically however with few theoretical explanations to said effectiveness for classification tasks. In this thesis, we aim to provide theoretical insights into RS ensemble classifiers to give a more in-depth understanding of the theoretical foundations of other ensemble classifiers. We investigate the conditions for norm-preservations in RS projections. Insights into this provide us with the theoretical basis for RS in algorithms that are based on the geometry of the data (i.e. clustering, nearest-neighbour). We then investigate the guarantees for the dot products of two random vectors after RS projection. This guarantee is useful to capture the geometric structure of a classification problem. We will then investigate the accuracy of a majority vote ensemble using a generalized Polya-Urn model, and how the parameters of the model are derived from diversity measures. We will discuss the practical implications of the model, explore the noise tolerance of ensembles, and give a plausible explanation for the effectiveness of ensembles. We will provide empirical corroboration for our main results with both synthetic and real-world high-dimensional data. We will also discuss the implications of our theory on other applications (i.e. compressive sensing). Based on our results, we will propose a method of building ensembles for Deep Neural Network image classifications using RS projections without needing to retrain the neural network, which showed improved accuracy and very good robustness to adversarial examples. Ultimately, we hope that the insights gained in this thesis would make in-roads towards the answer to a key open question for ensemble classifiers, "When will an ensemble of weak learners outperform a single carefully tuned learner?

Classification in high-dimensional feature spaces: Random subsample ensemble

Abstract-This paper presents application of machine learning ensembles, that randomly project the original high dimensional feature space onto multiple lower dimensional feature subspaces, to classification problems with highdimensional feature spaces. The motivation is to address challenges associated with algorithm scalability, data sparsity and information loss due to the so-called curse of dimensionality. The original high dimensional feature space is randomly projected onto a number of lower-dimensional feature subspaces. Each of these subspaces constitutes the domain of a classification subtask, and is associated with a base learner within an ensemble machine-learner context. Such an ensemble conceptualization is called as random subsample ensemble. Simulation results performed on data sets with up to 20,000 features indicate that the random subsample ensemble classifier performs comparably to other benchmark machine learners based on performance measures of prediction accuracy and cpu time. This finding establishes the feasibility of the ensemble and positions it to tackle classification problems with even much higher dimensional feature spaces