4,544 research outputs found
Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values
This work is motivated by the needs of predictive analytics on healthcare
data as represented by Electronic Medical Records. Such data is invariably
problematic: noisy, with missing entries, with imbalance in classes of
interests, leading to serious bias in predictive modeling. Since standard data
mining methods often produce poor performance measures, we argue for
development of specialized techniques of data-preprocessing and classification.
In this paper, we propose a new method to simultaneously classify large
datasets and reduce the effects of missing values. It is based on a multilevel
framework of the cost-sensitive SVM and the expected maximization imputation
method for missing values, which relies on iterated regression analyses. We
compare classification results of multilevel SVM-based algorithms on public
benchmark datasets with imbalanced classes and missing values as well as real
data in health applications, and show that our multilevel SVM-based method
produces fast, and more accurate and robust classification results.Comment: arXiv admin note: substantial text overlap with arXiv:1503.0625
Local Regularization Assisted Orthogonal Least Squares Regression
A locally regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsimonious or sparse regression models that generalize well. By associating each orthogonal weight in the regression model with an individual regularization parameter, the ability for the orthogonal least squares (OLS) model selection to produce a very sparse model with good generalization performance is greatly enhanced. Furthermore, with the assistance of local regularization, when to terminate the model selection procedure becomes much clearer. This LROLS algorithm has computational advantages over the recently introduced relevance vector machine (RVM) method
Convex Optimization for Binary Classifier Aggregation in Multiclass Problems
Multiclass problems are often decomposed into multiple binary problems that
are solved by individual binary classifiers whose results are integrated into a
final answer. Various methods, including all-pairs (APs), one-versus-all (OVA),
and error correcting output code (ECOC), have been studied, to decompose
multiclass problems into binary problems. However, little study has been made
to optimally aggregate binary problems to determine a final answer to the
multiclass problem. In this paper we present a convex optimization method for
an optimal aggregation of binary classifiers to estimate class membership
probabilities in multiclass problems. We model the class membership probability
as a softmax function which takes a conic combination of discrepancies induced
by individual binary classifiers, as an input. With this model, we formulate
the regularized maximum likelihood estimation as a convex optimization problem,
which is solved by the primal-dual interior point method. Connections of our
method to large margin classifiers are presented, showing that the large margin
formulation can be considered as a limiting case of our convex formulation.
Numerical experiments on synthetic and real-world data sets demonstrate that
our method outperforms existing aggregation methods as well as direct methods,
in terms of the classification accuracy and the quality of class membership
probability estimates.Comment: Appeared in Proceedings of the 2014 SIAM International Conference on
Data Mining (SDM 2014
Gibbs Max-margin Topic Models with Data Augmentation
Max-margin learning is a powerful approach to building classifiers and
structured output predictors. Recent work on max-margin supervised topic models
has successfully integrated it with Bayesian topic models to discover
discriminative latent semantic structures and make accurate predictions for
unseen testing data. However, the resulting learning problems are usually hard
to solve because of the non-smoothness of the margin loss. Existing approaches
to building max-margin supervised topic models rely on an iterative procedure
to solve multiple latent SVM subproblems with additional mean-field assumptions
on the desired posterior distributions. This paper presents an alternative
approach by defining a new max-margin loss. Namely, we present Gibbs max-margin
supervised topic models, a latent variable Gibbs classifier to discover hidden
topic representations for various tasks, including classification, regression
and multi-task learning. Gibbs max-margin supervised topic models minimize an
expected margin loss, which is an upper bound of the existing margin loss
derived from an expected prediction rule. By introducing augmented variables
and integrating out the Dirichlet variables analytically by conjugacy, we
develop simple Gibbs sampling algorithms with no restricting assumptions and no
need to solve SVM subproblems. Furthermore, each step of the
"augment-and-collapse" Gibbs sampling algorithms has an analytical conditional
distribution, from which samples can be easily drawn. Experimental results
demonstrate significant improvements on time efficiency. The classification
performance is also significantly improved over competitors on binary,
multi-class and multi-label classification tasks.Comment: 35 page
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