1,476 research outputs found
Learning Multiclass Rules with Class-Selective Rejection and Performance Constraints
International audienc
Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections
Supervised learning of microarray data is receiving much attention in recent years. Multiclass
cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However,
supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this
misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some
patients from one, some, or all classes in order to ensure a higher reliability while reducing time
and expense costs. Moreover, this classifier takes into account asymmetric penalties dependant
on each class and on each wrong or partially correct decision. It is based on ν-1-SVM coupled
with its regularization path and minimizes a general loss function defined in the class-selective
rejection scheme. The state of art multiclass algorithms can be considered as a particular case of
the proposed algorithm where the number of decisions is given by the classes and the loss function
is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class
selective rejection frameworks. Five genes selected datasets are used to assess the performance of
the proposed method. Results are discussed and accuracies are compared with those computed by
the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector
Machines classifiers
Comment on "Support Vector Machines with Applications"
Comment on ``Support Vector Machines with Applications'' [math.ST/0612817]Comment: Published at http://dx.doi.org/10.1214/088342306000000484 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
On the Consistency of Ordinal Regression Methods
Many of the ordinal regression models that have been proposed in the
literature can be seen as methods that minimize a convex surrogate of the
zero-one, absolute, or squared loss functions. A key property that allows to
study the statistical implications of such approximations is that of Fisher
consistency. Fisher consistency is a desirable property for surrogate loss
functions and implies that in the population setting, i.e., if the probability
distribution that generates the data were available, then optimization of the
surrogate would yield the best possible model. In this paper we will
characterize the Fisher consistency of a rich family of surrogate loss
functions used in the context of ordinal regression, including support vector
ordinal regression, ORBoosting and least absolute deviation. We will see that,
for a family of surrogate loss functions that subsumes support vector ordinal
regression and ORBoosting, consistency can be fully characterized by the
derivative of a real-valued function at zero, as happens for convex
margin-based surrogates in binary classification. We also derive excess risk
bounds for a surrogate of the absolute error that generalize existing risk
bounds for binary classification. Finally, our analysis suggests a novel
surrogate of the squared error loss. We compare this novel surrogate with
competing approaches on 9 different datasets. Our method shows to be highly
competitive in practice, outperforming the least squares loss on 7 out of 9
datasets.Comment: Journal of Machine Learning Research 18 (2017
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