Browse
Submit Data
Databases
API
Help

Dataset Information

36 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Principal weighted support vector machines for sufficient dimension reduction in binary classification.

ABSTRACT: Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are studied, and an efficient computing algorithm is proposed. Numerical examples demonstrate its performance in binary classification.

SUBMITTER: Shin SJ

PROVIDER: S-EPMC5793677 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Principal weighted support vector machines for sufficient dimension reduction in binary classification.

Shin Seung Jun SJ Wu Yichao Y Zhang Hao Helen HH Liu Yufeng Y

Biometrika 20170119 1

Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are s ...[more]

PMID: 29430027

Similar Datasets

Probability-enhanced sufficient dimension reduction for binary classification.

Project description:In high-dimensional data analysis, it is of primary interest to reduce the data dimensionality without loss of information. Sufficient dimension reduction (SDR) arises in this context, and many successful SDR methods have been developed since the introduction of sliced inverse regression (SIR) [Li (1991) Journal of the American Statistical Association 86, 316-327]. Despite their fast progress, though, most existing methods target on regression problems with a continuous response. For binary classification problems, SIR suffers the limitation of estimating at most one direction since only two slices are available. In this article, we develop a new and flexible probability-enhanced SDR method for binary classification problems by using the weighted support vector machine (WSVM). The key idea is to slice the data based on conditional class probabilities of observations rather than their binary responses. We first show that the central subspace based on the conditional class probability is the same as that based on the binary response. This important result justifies the proposed slicing scheme from a theoretical perspective and assures no information loss. In practice, the true conditional class probability is generally not available, and the problem of probability estimation can be challenging for data with large-dimensional inputs. We observe that, in order to implement the new slicing scheme, one does not need exact probability values and the only required information is the relative order of probability values. Motivated by this fact, our new SDR procedure bypasses the probability estimation step and employs the WSVM to directly estimate the order of probability values, based on which the slicing is performed. The performance of the proposed probability-enhanced SDR scheme is evaluated by both simulated and real data examples.

| S-EPMC4670268 | biostudies-literature

Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection.

Project description:Aqueous solubility is recognized as a critical parameter in both the early- and late-stage drug discovery. Therefore, in silico modeling of solubility has attracted extensive interests in recent years. Most previous studies have been limited in using relatively small data sets with limited diversity, which in turn limits the predictability of derived models. In this work, we present a support vector machines model for the binary classification of solubility by taking advantage of the largest known public data set that contains over 46?000 compounds with experimental solubility. Our model was optimized in combination with a reduction and recombination feature selection strategy. The best model demonstrated robust performance in both cross-validation and prediction of two independent test sets, indicating it could be a practical tool to select soluble compounds for screening, purchasing, and synthesizing. Moreover, our work may be used for comparative evaluation of solubility classification studies ascribe to the use of completely public resources.

| S-EPMC3047290 | biostudies-other

Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines.

Project description:Previous studies have reported abnormalities of white-matter diffusivity in pediatric bipolar disorder. However, it has not been established whether these abnormalities are able to distinguish individual subjects with pediatric bipolar disorder from healthy controls with a high specificity and sensitivity. Diffusion-weighted imaging scans were acquired from 16 youths diagnosed with DSM-IV bipolar disorder and 16 demographically matched healthy controls. Regional white matter tissue microstructural measurements such as fractional anisotropy, axial diffusivity and radial diffusivity were computed using an atlas-based approach. These measurements were used to 'train' a support vector machine (SVM) algorithm to predict new or 'unseen' subjects' diagnostic labels. The SVM algorithm predicted individual subjects with specificity=87.5%, sensitivity=68.75%, accuracy=78.12%, positive predictive value=84.62%, negative predictive value=73.68%, area under receiver operating characteristic curve (AUROC)=0.7812 and chi-square p-value=0.0012. A pattern of reduced regional white matter fractional anisotropy was observed in pediatric bipolar disorder patients. These results suggest that atlas-based diffusion weighted imaging measurements can distinguish individual pediatric bipolar disorder patients from healthy controls. Notably, from a clinical perspective these findings will contribute to the pathophysiological understanding of pediatric bipolar disorder.

| S-EPMC4631706 | biostudies-literature

miRBoost: boosting support vector machines for microRNA precursor classification.

Project description:Identification of microRNAs (miRNAs) is an important step toward understanding post-transcriptional gene regulation and miRNA-related pathology. Difficulties in identifying miRNAs through experimental techniques combined with the huge amount of data from new sequencing technologies have made in silico discrimination of bona fide miRNA precursors from non-miRNA hairpin-like structures an important topic in bioinformatics. Among various techniques developed for this classification problem, machine learning approaches have proved to be the most promising. However these approaches require the use of training data, which is problematic due to an imbalance in the number of miRNAs (positive data) and non-miRNAs (negative data), which leads to a degradation of their performance. In order to address this issue, we present an ensemble method that uses a boosting technique with support vector machine components to deal with imbalanced training data. Classification is performed following a feature selection on 187 novel and existing features. The algorithm, miRBoost, performed better in comparison with state-of-the-art methods on imbalanced human and cross-species data. It also showed the highest ability among the tested methods for discovering novel miRNA precursors. In addition, miRBoost was over 1400 times faster than the second most accurate tool tested and was significantly faster than most of the other tools. miRBoost thus provides a good compromise between prediction efficiency and execution time, making it highly suitable for use in genome-wide miRNA precursor prediction. The software miRBoost is available on our web server http://EvryRNA.ibisc.univ-evry.fr.

| S-EPMC4408786 | biostudies-literature

A novel dimension reduction algorithm based on weighted kernel principal analysis for gene expression data.

Project description:Gene expression data has the characteristics of high dimensionality and a small sample size and contains a large number of redundant genes unrelated to a disease. The direct application of machine learning to classify this type of data will not only incur a great time cost but will also sometimes fail to improved classification performance. To counter this problem, this paper proposes a dimension-reduction algorithm based on weighted kernel principal component analysis (WKPCA), constructs kernel function weights according to kernel matrix eigenvalues, and combines multiple kernel functions to reduce the feature dimensions. To further improve the dimensional reduction efficiency of WKPCA, t-class kernel functions are constructed, and corresponding theoretical proofs are given. Moreover, the cumulative optimal performance rate is constructed to measure the overall performance of WKPCA combined with machine learning algorithms. Naive Bayes, K-nearest neighbour, random forest, iterative random forest and support vector machine approaches are used in classifiers to analyse 6 real gene expression dataset. Compared with the all-variable model, linear principal component dimension reduction and single kernel function dimension reduction, the results show that the classification performance of the 5 machine learning methods mentioned above can be improved effectively by WKPCA dimension reduction.

| S-EPMC8513872 | biostudies-literature

EFFICIENT ESTIMATION IN SUFFICIENT DIMENSION REDUCTION.

Project description:We develop an efficient estimation procedure for identifying and estimating the central subspace. Using a new way of parameterization, we convert the problem of identifying the central subspace to the problem of estimating a finite dimensional parameter in a semiparametric model. This conversion allows us to derive an efficient estimator which reaches the optimal semiparametric efficiency bound. The resulting efficient estimator can exhaustively estimate the central subspace without imposing any distributional assumptions. Our proposed efficient estimation also provides a possibility for making inference of parameters that uniquely identify the central subspace. We conduct simulation studies and a real data analysis to demonstrate the finite sample performance in comparison with several existing methods.

| S-EPMC3777433 | biostudies-literature

Sufficient dimension reduction for censored regressions.

Project description:Methodology of sufficient dimension reduction (SDR) has offered an effective means to facilitate regression analysis of high-dimensional data. When the response is censored, however, most existing SDR estimators cannot be applied, or require some restrictive conditions. In this article, we propose a new class of inverse censoring probability weighted SDR estimators for censored regressions. Moreover, regularization is introduced to achieve simultaneous variable selection and dimension reduction. Asymptotic properties and empirical performance of the proposed methods are examined.

| S-EPMC3018713 | biostudies-literature

Sufficient dimension reduction for censored predictors.

Project description:Motivated by a study conducted to evaluate the associations of 51 inflammatory markers and lung cancer risk, we propose several approaches of varying computational complexity for analyzing multiple correlated markers that are also censored due to lower and/or upper limits of detection, using likelihood-based sufficient dimension reduction (SDR) methods. We extend the theory and the likelihood-based SDR framework in two ways: (i) we accommodate censored predictors directly in the likelihood, and (ii) we incorporate variable selection. We find linear combinations that contain all the information that the correlated markers have on an outcome variable (i.e., are sufficient for modeling and prediction of the outcome) while accounting for censoring of the markers. These methods yield efficient estimators and can be applied to any type of outcome, including continuous and categorical. We illustrate and compare all methods using data from the motivating study and in simulations. We find that explicitly accounting for the censoring in the likelihood of the SDR methods can lead to appreciable gains in efficiency and prediction accuracy, and also outperformed multiple imputations combined with standard SDR.

| S-EPMC6543825 | biostudies-literature

Sufficient dimension reduction for compositional data.

Project description:Recent efforts to characterize the human microbiome and its relation to chronic diseases have led to a surge in statistical development for compositional data. We develop likelihood-based sufficient dimension reduction methods (SDR) to find linear combinations that contain all the information in the compositional data on an outcome variable, i.e., are sufficient for modeling and prediction of the outcome. We consider several models for the inverse regression of the compositional vector or transformations of it, as a function of outcome. They include normal, multinomial, and Poisson graphical models that allow for complex dependencies among observed counts. These methods yield efficient estimators of the reduction and can be applied to continuous or categorical outcomes. We incorporate variable selection into the estimation via penalties and address important invariance issues arising from the compositional nature of the data. We illustrate and compare our methods and some established methods for analyzing microbiome data in simulations and using data from the Human Microbiome Project. Displaying the data in the coordinate system of the SDR linear combinations allows visual inspection and facilitates comparisons across studies.

| S-EPMC8677486 | biostudies-literature

Comparing logistic regression, support vector machines, and permanental classification methods in predicting hypertension.

Project description:In this paper, we compare logistic regression and 2 other classification methods in predicting hypertension given the genotype information. We use logistic regression analysis in the first step to detect significant single-nucleotide polymorphisms (SNPs). In the second step, we use the significant SNPs with logistic regression, support vector machines (SVMs), and a newly developed permanental classification method for prediction purposes. We also detect rare variants and investigate their impact on prediction. Our results show that SVMs and permanental classification both outperform logistic regression, and they are comparable in predicting hypertension status.

| S-EPMC4143639 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data