604 research outputs found
Multilabel Consensus Classification
In the era of big data, a large amount of noisy and incomplete data can be
collected from multiple sources for prediction tasks. Combining multiple models
or data sources helps to counteract the effects of low data quality and the
bias of any single model or data source, and thus can improve the robustness
and the performance of predictive models. Out of privacy, storage and bandwidth
considerations, in certain circumstances one has to combine the predictions
from multiple models or data sources to obtain the final predictions without
accessing the raw data. Consensus-based prediction combination algorithms are
effective for such situations. However, current research on prediction
combination focuses on the single label setting, where an instance can have one
and only one label. Nonetheless, data nowadays are usually multilabeled, such
that more than one label have to be predicted at the same time. Direct
applications of existing prediction combination methods to multilabel settings
can lead to degenerated performance. In this paper, we address the challenges
of combining predictions from multiple multilabel classifiers and propose two
novel algorithms, MLCM-r (MultiLabel Consensus Maximization for ranking) and
MLCM-a (MLCM for microAUC). These algorithms can capture label correlations
that are common in multilabel classifications, and optimize corresponding
performance metrics. Experimental results on popular multilabel classification
tasks verify the theoretical analysis and effectiveness of the proposed
methods
Improved Multi-Class Cost-Sensitive Boosting via Estimation of the Minimum-Risk Class
We present a simple unified framework for multi-class cost-sensitive boosting.
The minimum-risk class is estimated directly, rather than via an approximation
of the posterior distribution. Our method jointly optimizes binary weak learners
and their corresponding output vectors, requiring classes to share features at each
iteration. By training in a cost-sensitive manner, weak learners are invested in separating
classes whose discrimination is important, at the expense of less relevant
classification boundaries. Additional contributions are a family of loss functions
along with proof that our algorithm is Boostable in the theoretical sense, as well
as an efficient procedure for growing decision trees for use as weak learners. We
evaluate our method on a variety of datasets: a collection of synthetic planar data,
common UCI datasets, MNIST digits, SUN scenes, and CUB-200 birds. Results
show state-of-the-art performance across all datasets against several strong baselines,
including non-boosting multi-class approaches
Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers
ANR-2010-COSI-002In subset ranking, the goal is to learn a ranking function that approximates a gold standard partial ordering of a set of objects (in our case, a set of documents retrieved for the same query). The partial ordering is given by relevance labels representing the relevance of documents with respect to the query on an absolute scale. Our approach consists of three simple steps. First, we train standard multi-class classifiers (AdaBoost.MH and multi-class SVM) to discriminate between the relevance labels. Second, the posteriors of multi-class classifiers are calibrated using probabilistic and regression losses in order to estimate the Bayes-scoring function which optimizes the Normalized Discounted Cumulative Gain (NDCG). In the third step, instead of selecting the best multi-class hyperparameters and the best calibration, we mix all the learned models in a simple ensemble scheme. Our extensive experimental study is itself a substantial contribution. We compare most of the existing learning-to-rank techniques on all of the available large-scale benchmark data sets using a standardized implementation of the NDCG score. We show that our approach is competitive with conceptually more complex listwise and pairwise methods, and clearly outperforms them as the data size grows. As a technical contribution, we clarify some of the confusing results related to the ambiguities of the evaluation tools, and propose guidelines for future studies
Multi-class Classification with Machine Learning and Fusion
Treball realitzat a TELECOM ParisTech i EADS FranceMulti-class classification is the core issue of many pattern recognition tasks. Several applications
require high-end machine learning solutions to provide satisfying results in operational contexts. However,
most efficient ones, like SVM or Boosting, are generally mono-class, which introduces the problem of
translating a global multi-class problem is several binary problems, while still being able to provide at the
end an answer to the original multi-class issue.
Present work aims at providing a solution to this multi-class problematic, by introducing a complete
framework with a strong probabilistic and structured basis. It includes the study of error correcting output
codes correlated with the definition of an optimal subdivision of the multi-class issue in several binary
problems, in a complete automatic way. Machine learning algorithms are studied and benchmarked to
facilitate and justify the final selection. Coupling of automatically calibrated classifiers output is obtained by
applying iterative constrained regularisations, and a logical temporal fusion is applied on temporal-redundant
data (like tracked vehicles) to enhance performances. Finally, ranking scores are computed to optimize
precision and recall is ranking-based systems.
Each step of the previously described system has been analysed from a theoretical an empirical
point of view and new contributions are introduced, so as to obtain a complete mathematically coherent
framework which is both generic and easy-to-use, as the learning procedure is almost completely automatic.
On top of that, quantitative evaluations on two completely different datasets have assessed both the
exactitude of previous assertions and the improvements that were achieved compared to previous methods
Probabilistic multiple kernel learning
The integration of multiple and possibly heterogeneous information sources for an overall decision-making process has been an open and unresolved research direction in computing science since its very beginning. This thesis attempts to address parts of that direction by proposing probabilistic data integration algorithms for multiclass decisions where an observation of interest is assigned to one of many categories based on a plurality of information channels
Recommended from our members
Parallelizing support vector machines for scalable image annotation
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large.
In this thesis distributed computing paradigms have been investigated to speed up SVM training, by partitioning a large training dataset into small data chunks and process each chunk in parallel utilizing the resources of a cluster of computers. A resource aware parallel SVM algorithm is introduced for large scale image annotation in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of the algorithm in heterogeneous computing environments.
SVM was initially designed for binary classifications. However, most classification problems arising in domains such as image annotation usually involve more than two classes. A resource aware parallel multiclass SVM algorithm for large scale image annotation in parallel using a cluster of computers is introduced.
The combination of classifiers leads to substantial reduction of classification error in a wide range of applications. Among them SVM ensembles with bagging is shown to outperform a single SVM in terms of classification accuracy. However, SVM ensembles training are notably a computationally intensive process especially when the number replicated samples based on bootstrapping is large. A distributed SVM ensemble algorithm for image annotation is introduced which re-samples the training data based on bootstrapping and training SVM on each sample in parallel using a cluster of computers.
The above algorithms are evaluated in both experimental and simulation environments showing that the distributed SVM algorithm, distributed multiclass SVM algorithm, and distributed SVM ensemble algorithm, reduces the training time significantly while maintaining a high level of accuracy in classifications
- …