Journal ArticleDOI
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection
TLDR
The basic taxonomy of feature selection is presented, and the state-of-the-art gene selection methods are reviewed by grouping the literatures into three categories: supervised, unsupervised, and semi-supervised.Abstract:
Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up to hundreds of thousands of features with relatively small sample size. Because learning algorithms usually do not work well with this kind of data, a challenge to reduce the data dimensionality arises. A huge number of gene selection are applied to select a subset of relevant features for model construction and to seek for better cancer classification performance. This paper presents the basic taxonomy of feature selection, and also reviews the state-of-the-art gene selection methods by grouping the literatures into three categories: supervised, unsupervised, and semi-supervised. The comparison of experimental results on top 5 representative gene expression datasets indicates that the classification accuracy of unsupervised and semi-supervised feature selection is competitive with supervised feature selection.read more
Citations
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Journal ArticleDOI
Feature Selection: A Data Perspective
TL;DR: This survey revisits feature selection research from a data perspective and reviews representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data, and categorizes them into four main groups: similarity- based, information-theoretical-based, sparse-learning-based and statistical-based.
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
A Survey on semi-supervised feature selection methods
TL;DR: In this paper, semi-supervised feature selection methods are fully investigated and two taxonomies of these methods are presented based on two different perspectives which represent the hierarchical structure of semi- supervised feature Selection methods.
Journal ArticleDOI
A review of unsupervised feature selection methods
TL;DR: A comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature is provided and a taxonomy of these methods is presented.
Journal ArticleDOI
Feature Selection: A Data Perspective
TL;DR: Feature selection, as a data preprocessing strategy, has proven to be effective and efficient in preparing data (especially high-dimensional data) for various data mining and machine learning problems.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Molecular portraits of human breast tumours
Charles M. Perou,Therese Sørlie,Michael B. Eisen,Matt van de Rijn,Stefanie S. Jeffrey,Christian A. Rees,Jonathan R. Pollack,Douglas T. Ross,Hilde Johnsen,Lars A. Akslen,Øystein Fluge,Alexander Pergamenschikov,Cheryl A. Williams,Shirley Zhu,Per Eystein Lønning,Anne Lise Børresen-Dale,Patrick O. Brown,David Botstein +17 more
TL;DR: Variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals were characterized using complementary DNA microarrays representing 8,102 human genes, providing a distinctive molecular portrait of each tumour.
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.
Journal ArticleDOI
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.