Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

Neighborhood based sample and feature selection for SVM classification learning

Support vector machines (SVMs) are a class of popular classification algorithms for their high generalization ability. However, it is time-consuming to train SVMs with a large set of learning samples. Improving learning efficiency is one of most important research tasks on SVMs. It is known that although there are many candidate training samples in some learning tasks, only the samples near pla...

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Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines

In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...

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Feature Selection and Classification of Microarray Gene Expression Data of Ovarian Carcinoma Patients using Weighted Voting Support Vector Machine

We can reach by DNA microarray gene expression to such wealth of information with thousands of variables (genes). Analysis of this information can show genetic reasons of disease and tumor differences. In this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of...

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Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines

In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...

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Semi-supervised SVM-based Feature Selection for Cancer Classification using Microarray Gene Expression Data

Gene expression data always suffer from the high dimensionality issue, therefore feature selection becomes a fundamental tool in the analysis of cancer classification. Basically, the data can be collected easily without providing the label information, which is quite useful in improving the accuracy of the classification. Label information usually difficult to obtain as the labelling processes ...

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Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

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چکیده

منابع مشابه

Neighborhood based sample and feature selection for SVM classification learning

Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines

Feature Selection and Classification of Microarray Gene Expression Data of Ovarian Carcinoma Patients using Weighted Voting Support Vector Machine

Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines

Semi-supervised SVM-based Feature Selection for Cancer Classification using Microarray Gene Expression Data

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