Results 1  10
of
13
Polychotomous Classification with Pairwise Classifiers: a New Voting Principle
"... A new principle for performing polychotomous classification with pairwise classifiers is introduced: if pairwise classifier N_ij , trained to discriminate between classes i and j, responds "i" for an input x from an unknown class (not necessarily i or j), one can at best conclude that x &a ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
A new principle for performing polychotomous classification with pairwise classifiers is introduced: if pairwise classifier N_ij , trained to discriminate between classes i and j, responds "i" for an input x from an unknown class (not necessarily i or j), one can at best conclude that x
Classification by pairwise coupling
, 1998
"... We discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together. The coupling model is similar to the BradleyTerry method for paired comparisons. We study the nature of the class probability estim ..."
Abstract

Cited by 378 (0 self)
 Add to MetaCart
We discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together. The coupling model is similar to the BradleyTerry method for paired comparisons. We study the nature of the class probability
Classification Of Gene Expression Data by Pairwise Comparisons
"... In this report, we discuss a strategy to produce simple and easy to understand classifiers for polychotomous classification of gene expression data. In particular, we propose to decompose the Kclass prediction problem into the () possible 2class ones, solve those and combine in appropriate manner ..."
Abstract
 Add to MetaCart
In this report, we discuss a strategy to produce simple and easy to understand classifiers for polychotomous classification of gene expression data. In particular, we propose to decompose the Kclass prediction problem into the () possible 2class ones, solve those and combine in appropriate manner
BIOINFORMATICS Structured Polychotomous Machine Diagnosis of Multiple Cancer Types Using Gene Expression
"... Motivation: The problem of class prediction has received a tremendous amount of attention in the literature recently. In the context of DNA microarrays, where the task is to classify and predict the diagnostic category of a sample on the basis of its gene expression profile, a problem of particular ..."
Abstract
 Add to MetaCart
importance is the diagnosis of cancer type based on microarray data. One method of classification which has been very successful in cancer diagnosis is the support vector machine. The latter has been shown (through simulations) to be superior in comparison to other methods, such as classical
Learning valued preference structures for solving classification problems
 Fuzzy Sets and Systems
"... This paper introduces a new approach to classification which combines pairwise decomposition techniques with ideas and tools from fuzzy preference modeling. More specifically, our approach first decomposes a polychotomous classification problem involving m classes into an ensemble of binary problems ..."
Abstract

Cited by 14 (3 self)
 Add to MetaCart
This paper introduces a new approach to classification which combines pairwise decomposition techniques with ideas and tools from fuzzy preference modeling. More specifically, our approach first decomposes a polychotomous classification problem involving m classes into an ensemble of binary
Pairwise Classifier Combination using Belief Functions
, 2006
"... In the socalled pairwise approach to polychotomous classification, a multiclass problem is solved by combining classifiers trained to discriminate between each pair of classes. In this paper, this approach is revisited in the framework of the DempsterShafer theory of belief functions, a nonproba ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
In the socalled pairwise approach to polychotomous classification, a multiclass problem is solved by combining classifiers trained to discriminate between each pair of classes. In this paper, this approach is revisited in the framework of the DempsterShafer theory of belief functions, a non
FR3: A Fuzzy Rule Learner for Inducing Reliable Classifiers
"... This paper introduces a fuzzy rulebased classification method called FR3, which is short for Fuzzy Round Robin RIPPER. In the context of polychotomous classification, it uses a fuzzy extension of the wellknown RIPPER algorithm as a base learner within a round robin scheme. A key feature of FR3 is ..."
Abstract
 Add to MetaCart
This paper introduces a fuzzy rulebased classification method called FR3, which is short for Fuzzy Round Robin RIPPER. In the context of polychotomous classification, it uses a fuzzy extension of the wellknown RIPPER algorithm as a base learner within a round robin scheme. A key feature of FR3
Variational Bayesian multinomial probit regression with Gaussian process priors
 Neural Computation
, 2005
"... It is well known in the statistics literature that augmenting binary and polychotomous response models with Gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over regression ..."
Abstract

Cited by 60 (17 self)
 Add to MetaCart
It is well known in the statistics literature that augmenting binary and polychotomous response models with Gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over
Creating Imputation Classes Using Classification Tree Methodology
"... Virtually all surveys encounter some level of item nonresponse. To address this potential source of bias, practitioners often use imputation to replace missing values with valid values through some form of stochastic modeling. In order to improve the reliabilities of such models, imputation classes ..."
Abstract
 Add to MetaCart
examines an alternative methodology used to form imputation classes, nonparametric classification trees where the splitting rules are based on the Gini index of impurity, which is one possible splitting rule used in Classification and Regression Trees (CART). In addition to a brief description of the two
A Versatile Framework for Labelling Imagery With a Large Number of Classes
"... Conventional methods for feature selection use some kind of separability criteria or classification accuracy for computing the relevance of a feature subset to the classification task. In twoclass problems, this approach may be suitable, but for problems such as character recognition with 26 classe ..."
Abstract

Cited by 15 (8 self)
 Add to MetaCart
Conventional methods for feature selection use some kind of separability criteria or classification accuracy for computing the relevance of a feature subset to the classification task. In twoclass problems, this approach may be suitable, but for problems such as character recognition with 26
Results 1  10
of
13