Journal Article10.1016/J.PATREC.2005.12.018
Random subspace method for multivariate feature selection
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TL;DR: A new multivariate search technique is introduced, that is less sensitive to the noise in the data and computationally feasible as well and the robustness and reliability of the novel multivariate feature selection method are compared.
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About: This article is published in Pattern Recognition Letters. The article was published on 15 Jul 2006. The article focuses on the topics: Univariate & Feature selection.
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Citations
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References
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.
Gene expression profiling predicts clinical outcome of breast cancer
Laura J. van't Veer,Hongyue Dai,Marc J. van de Vijver,Yudong D. He,Augustinus A. M. Hart,Mao Mao,Hans Peterse,Karin van der Kooy,Matthew J. Marton,Anke T. Witteveen,George J. Schreiber,Ron M. Kerkhoven,Christopher J. Roberts,Peter S. Linsley,René Bernards,Stephen H. Friend +15 more
TL;DR: DNA microarray analysis on primary breast tumours of 117 young patients is used and supervised classification is applied to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis, providing a strategy to select patients who would benefit from adjuvant therapy.
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.
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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.
The random subspace method for constructing decision forests
TL;DR: A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
7K