Journal ArticleDOI
A novel features ranking metric with application to scalable visual and bioinformatics data classification
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TLDR
A Max-Relevance-Max-Distance (MRMD) feature ranking method, which balances accuracy and stability of feature ranking and prediction task, and runs faster than other filtering and wrapping methods, such as mRMR and Information Gain.About:
This article is published in Neurocomputing.The article was published on 2016-01-15. It has received 344 citations till now. The article focuses on the topics: Dimensionality reduction & Data classification.read more
Citations
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Journal ArticleDOI
Feature selection in machine learning: A new perspective
TL;DR: This study discusses several frequently-used evaluation measures for feature selection, and surveys supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering.
Journal ArticleDOI
Predicting Diabetes Mellitus With Machine Learning Techniques.
TL;DR: The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used and principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) was used to reduce the dimensionality.
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Tumor origin detection with tissue-specific miRNA and DNA methylation markers.
TL;DR: Novel selection strategies to identify highly tissue‐specific CpG sites are introduced and the random forest approach is used to construct the classifiers that can efficiently predict the origin of tumors.
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Performance of machine-learning scoring functions in structure-based virtual screening.
TL;DR: A new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets that provides much better prediction of measured binding affinity than Vina.
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Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.
TL;DR: It is found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases.
References
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Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
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.
Journal ArticleDOI
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Feature selection based on mutual information: criteria ofmax-dependency, max-relevance, and min-redundancy
TL;DR: This work derives an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection, and presents a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers).
Journal ArticleDOI
A review of feature selection techniques in bioinformatics
TL;DR: A basic taxonomy of feature selection techniques is provided, providing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.
Proceedings ArticleDOI
Locality-constrained Linear Coding for image classification
TL;DR: This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM, using the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation.