Journal ArticleDOI
Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products
TLDR
RFE outperforms SVM-RFE and KWS on the task of finding small subsets of features with high discrimination levels on PTR-MS data sets, and it is shown how selection probabilities and features co-occurrence can be used to highlight the most relevant features for discrimination.About:
This article is published in Chemometrics and Intelligent Laboratory Systems.The article was published on 2006-09-15. It has received 429 citations till now. The article focuses on the topics: Feature selection & Feature (computer vision).read more
Citations
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疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
“Bioinformatics” 특집을 내면서
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Journal ArticleDOI
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
Bjoern H. Menze,B. Michael Kelm,Ralf Masuch,Uwe Himmelreich,Peter Bachert,Wolfgang Petrich,Wolfgang Petrich,Fred A. Hamprecht,Fred A. Hamprecht +8 more
TL;DR: The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data, but – on an optimal subset of features – the regularized classifiers might be preferable over the random Forest classifier, in spite of their limitation to model linear dependencies only.
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
Bjoern H. Menze,Bernd Michael Kelm,Ralf Masuch,Uwe Himmerlreich,Peter Bachert,Wolfgang Petrich,Fred A. Hamprecht +6 more
Journal ArticleDOI
Whale optimization approaches for wrapper feature selection
Majdi Mafarja,Seyedali Mirjalili +1 more
TL;DR: A new wrapper feature selection approach is proposed based on Whale Optimization Algorithm based on the influence of using the Tournament and Roulette Wheel selection mechanisms instead of using a random operator in the searching process to search the optimal feature subsets for classification purposes.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
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
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.