L2,p-norm and sample constraint based feature selection and classification for AD diagnosis

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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Feature Selection for Classification under Anonymity Constraint

Over the last decade, proliferation of various online platforms and their increasing adoption by billions of users have heightened the privacy risk of a user enormously. In fact, security researchers have shown that sparse microdata containing information about online activities of a user although anonymous, can still be used to disclose the identity of the user by cross-referencing the data wi...

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SVD Based Feature Selection and Sample Classification of Proteomic Data

Feature selection becomes a central task when ’signature’ profiles specific to a pathological status have to be extracted from high dimensional gene expression or proteomic data. In the present paper, we propose a feature selection method based on Singular Value Decomposition (SVD) and apply it to SELDI-TOF/MS proteomic data from a cohort of Type 2 Diabetics affected by Glomerulosclerosis and M...

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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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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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L2,p-norm and sample constraint based feature selection and classification for AD diagnosis

نویسندگان

چکیده

منابع مشابه

Neighborhood based sample and feature selection for SVM classification learning

Feature Selection for Classification under Anonymity Constraint

SVD Based Feature Selection and Sample Classification of Proteomic Data

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

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

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