Open AccessJournal Article
Variable selection using svm based criteria
TLDR
New methods to evaluate variable subset relevance with a view to variable selection based on weight vector derivative achieves good results and performs consistently well over the datasets used.Abstract:
We propose new methods to evaluate variable subset relevance with a view to variable selection. Relevance criteria are derived from Support Vector Machines and are based on weight vector ||w||2 or generalization error bounds sensitivity with respect to a variable. Experiments on linear and non-linear toy problems and real-world datasets have been carried out to assess the effectiveness of these criteria. Results show that the criterion based on weight vector derivative achieves good results and performs consistently well over the datasets we used.read more
Citations
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An introduction to variable and feature selection
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TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
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Variable selection using random forests
TL;DR: This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection, and proposes a strategy involving a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.
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Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture
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TL;DR: Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed Broad Learning System.
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Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
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Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme.
Evangelia I. Zacharaki,Evangelia I. Zacharaki,Sumei Wang,Sanjeev Chawla,Dong Soo Yoo,Dong Soo Yoo,Ronald L. Wolf,Elias R. Melhem,Christos Davatzikos +8 more
TL;DR: A computer‐assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis and consists of several steps including region‐of‐interest definition, feature extraction, feature selection, and classification.
References
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Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Journal ArticleDOI
Wrappers for feature subset selection
Ron Kohavi,George H. John +1 more
TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.
Journal ArticleDOI
Gene Selection for Cancer Classification using Support Vector Machines
TL;DR: In this article, a Support Vector Machine (SVM) method based on recursive feature elimination (RFE) was proposed to select a small subset of genes from broad patterns of gene expression data, recorded on DNA micro-arrays.