Probabilistic support vector machines for classification of noise affected data

Li, Han-Xiong; Yang, Jinglin; Zhang, Geng; Fan, Bi

doi:10.1016/j.ins.2012.09.041

Cited by 54 publications

(15 citation statements)

References 32 publications

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“…Given the data D ¼ fs j ; y j g N j¼1 ; y j 2 fÀ 1; 1g, s j 2 R M , the original SVM solves the following problem to maximize the margin between two classes [28]:…”

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confidence: 99%

A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction

2020

PLoS ONE

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Background The wide adoption of electronic health records (EHR) system has provided vast opportunities to advance health care services. However, the prevalence of missing values in EHR system poses a great challenge on data analysis to support clinical decision-making. The objective of this study is to develop a new methodological framework that can address the missing data challenge and provide a reliable tool to predict the hospital readmission among Heart Failure patients. Methods We used Gaussian Process Latent Variable Model (GPLVM) to impute the missing values. Specifically, a lower dimensional embedding was learned from a small complete dataset and then used to impute the missing values in the incomplete dataset. The GPLVM-based missing data imputation can provide both the mean estimate and the uncertainty associated with the mean estimate. To incorporate the uncertainty in prediction, a constrained support vector machine (cSVM) was developed to obtain robust predictions. We first sampled multiple datasets from the distributions of input uncertainty and trained a support vector machine for each dataset. Then an optimal classifier was identified by selecting the support vectors that maximize the separation margin of a newly sampled dataset and minimize the similarity with the pre-trained support vectors. Results The proposed model was derived and validated using Physionet MIMIC-III clinical database. The GPLVM imputation provided normalized mean absolute errors of 0.11 and 0.12 respectively when 20% and 30% of instances contained missing values, and the confidence bounds of the estimations captures 97% of the true values. The cSVM model provided an

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“…Given the data D ¼ fs j ; y j g N j¼1 ; y j 2 fÀ 1; 1g, s j 2 R M , the original SVM solves the following problem to maximize the margin between two classes [28]:…”

mentioning

confidence: 99%

A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction

2020

PLoS ONE

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“…47 Furthermore, because the decision boundary learned by an SVM is defined by the support vectors only, the classification boundary is highly sensitive to noise in these vectors. In other words, small variations in support vectors can produce large variations in the decision boundary, especially for sparse datasets 18 . We implement the SVM classifier using Weka 48 , a Java-based, freely available platform that provides a collection of machine learning algorithms for data mining tasks.…”

Section: Methodsmentioning

confidence: 99%

“…Support vectors for the imaging measures establish the location and orientation of the hyperplane in feature space that optimally separates those disorders. The diagnostic accuracy of the support vectors and their associated hyperplane therefore is sensitive both to how representative the sampling of participants is of the general population of persons who have those disorders and to the errors in the imaging measures of their brains, especially when the measures are available from only a relatively small sample of participants 18 .…”

Section: Introductionmentioning

confidence: 99%

Using Copula distributions to support more accurate imaging-based diagnostic classifiers for neuropsychiatric disorders

Bansal

Hao

Liu

et al. 2014

Magnetic Resonance Imaging

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Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. >10) relative to the number of participants who provide the MRI data (<100). Sparse data in a high dimensional space increases the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging measures or their multivariate Gaussian distributions. Thus, our findings demonstrate that estimated multivariate Copula distributions can generate dense sets of brain imaging measures that can in turn be used to train classifiers, and those classifiers are significantly more accurate and more reproducible than are those generated using real-world imaging measures alone.

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“…It has been widely used for data analysis in many fields, including anthropology, biology, economics, marketing, and medicine. Typical applications include disease classification, doc-ument retrieval, image processing, market segmentation, scene analysis, and web access pattern analysis (Guan et al, 2013;Li et al, 2013;Shrivastava and Tyagi, 2014;Hruschka et al, 2006;Li et al, 2006;Lingras et al, 2005;Choi et al, 2012).…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

Holo-Entropy Based Categorical Data Hierarchical Clustering

Chen¹,

Qin²,

Wang³

2017

Informatica

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Clustering high-dimensional data is a challenging task in data mining, and clustering high-dimensional categorical data is even more challenging because it is more difficult to measure the similarity between categorical objects. Most algorithms assume feature independence when computing similarity between data objects, or make use of computationally demanding techniques such as PCA for numerical data. Hierarchical clustering algorithms are often based on similarity measures computed on a common feature space, which is not effective when clustering highdimensional data. Subspace clustering algorithms discover feature subspaces for clusters, but are mostly partition-based; i.e. they do not produce a hierarchical structure of clusters. In this paper, we propose a hierarchical algorithm for clustering high-dimensional categorical data, based on a recently proposed information-theoretical concept named holo-entropy. The algorithm proposes new ways of exploring entropy, holo-entropy and attribute weighting in order to determine the feature subspace of a cluster and to merge clusters even though their feature subspaces differ. The algorithm is tested on UCI datasets, and compared with several state-of-the-art algorithms. Experimental results show that the proposed algorithm yields higher efficiency and accuracy than the competing algorithms and allows higher reproducibility.

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Probabilistic support vector machines for classification of noise affected data

Cited by 54 publications

References 32 publications

A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction

A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction

Using Copula distributions to support more accurate imaging-based diagnostic classifiers for neuropsychiatric disorders

Holo-Entropy Based Categorical Data Hierarchical Clustering

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