An Output Grouping Based Approach to Multiclass Classification Using Support Vector Machines

Zhao, Xuan; Guan, Steven; Man, Ka Lok

doi:10.1007/978-981-10-1536-6_51

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…Next, we extend the previous method via the “one-vs-rest” method [ 51 ], which decomposes a multi-classification problem into multiple binary classification problems, and each binary classifier is trained independently. For every sample, only the most “confident” model is selected to make the prediction.…”

Section: Resultsmentioning

confidence: 99%

Scalable analysis of multi-modal biomedical data

et al. 2021

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Background Targeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes. Solution To address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types. Performance We outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on “flattening” complex data structures, and runs efficiently when alternative approaches are unable to perform at all.

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Section: Resultsmentioning

confidence: 99%

Scalable analysis of multi-modal biomedical data

et al. 2021

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“…33 According to the "One-vs-All" method, we had to construct N-independent binary classifiers, so the every classifier will separate a specific class feature vectors from all other class's feature vectors. 34 According to the "One-vs-One" (also known as "All-vs-All") method, we had to construct NðN − 1Þ independent binary classifiers, each of which will separate i'th class feature vectors from j'th class feature vectors. 35 The latter method was shown to provide the better results.…”

Section: Resultsmentioning

confidence: 99%

Exhaled air analysis using wideband wave number tuning range infrared laser photoacoustic spectroscopy

et al. 2017

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The infrared laser photoacoustic spectroscopy (LPAS) and the pattern-recognition-based approach for noninvasive express diagnostics of pulmonary diseases on the basis of absorption spectra analysis of the patient’s exhaled air are presented. The study involved lung cancer patients ( N = 9 ), patients with chronic obstructive pulmonary disease ( N = 12 ), and a control group of healthy, nonsmoking volunteers ( N = 11 ). The analysis of the measured absorption spectra was based at first on reduction of the dimension of the feature space using principal component analysis; thereafter, the dichotomous classification was carried out using the support vector machine. The gas chromatography–mass spectrometry method (GC–MS) was used as the reference. The estimated mean value of the sensitivity of exhaled air sample analysis by the LPAS in dichotomous classification was not less than 90% and specificity was not less than 69%; the analogous results of analysis by GC–MS were 68% and 60%, respectively. Also, the approach to differential diagnostics based on the set of SVM classifiers usage is presented.

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“…Thirdly, RST embeds discriminative information into the input space (feature space) from the output space (class labels). If some outputs (classes) are similar [25] or some samples are mislabelled, RST will make less ideal transformations. …”

Section: Resultsmentioning

confidence: 99%

A subspace recursive and selective feature transformation method for classification tasks

Zhao

Guan

2017

Big Data Anal

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Background: Practitioners and researchers often found the intrinsic representations of high-dimensional problems has much fewer independent variables. However such intrinsic structure may not be easily discovered due to noises and other factors. A supervised transformation scheme RST is proposed to transform features into lower dimensional spaces for classification tasks. The proposed algorithm recursively and selectively transforms the features guided by the output variables. Results: We compared the classification performance of linear classifier and random forest classifier on the original data sets, data sets being transformed with RST and data sets being transformed by principle component analysis and linear discriminant analysis. On 7 out 8 data sets RST shows superior classification performance with linear classifiers but less ideal with random forest classifiers. Conclusions: Our test shows the proposed method's capability to reduce features dimensions in general classification tasks and preserve useful information using linear transformations. Some limitations of this method are also pointed out.

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An Output Grouping Based Approach to Multiclass Classification Using Support Vector Machines

Cited by 5 publications

References 8 publications

Scalable analysis of multi-modal biomedical data

Scalable analysis of multi-modal biomedical data

Exhaled air analysis using wideband wave number tuning range infrared laser photoacoustic spectroscopy

A subspace recursive and selective feature transformation method for classification tasks

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