Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data
Pradipta Maji,Sushmita Paul +1 more
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TLDR
A new feature selection algorithm is presented based on rough set theory that selects a set of genes from microarray data by maximizing the relevance and significance of the selected genes.About:
This article is published in International Journal of Approximate Reasoning.The article was published on 2011-03-01 and is currently open access. It has received 130 citations till now. The article focuses on the topics: Test set & Rough set.read more
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Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
TL;DR: A real world problem presented in the ECDBL’2014 Big Data competition is used to provide a thorough analysis on the application of some preprocessing techniques, their combination and their performance.
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Feature selection with test cost constraint
Fan Min,Qinghua Hu,William Zhu +2 more
TL;DR: The feature selection with test cost constraint problem for this issue, which has a simple form while described as a constraint satisfaction problem (CSP), and some existing feature selection problems in rough sets, especially in decision-theoretic rough sets are defined from the viewpoint of CSP.
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A novel feature selection method considering feature interaction
TL;DR: The results on the eight real world datasets indicate that IWFS not only efficiently reduces the dimensionality of feature space, but also offers the highest average accuracy for all the three classification algorithms.
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Online feature selection for high-dimensional class-imbalanced data
TL;DR: This work formalizes the problem of online streaming feature selection for class imbalanced data, and presents an efficient online feature selection framework regarding the dependency between condition features and decision classes, and proposes a new algorithm of Online Feature Selection based on the Dependency in K nearest neighbors, called K-OFSD.
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Hyperspectral Band Selection Based on Rough Set
TL;DR: Three state-of-the-art methods used in the remote sensing literature are analyzed for comparison and the results point to the superiority of the proposed rough-set-based supervised technique, especially when a small number of bands are to be selected.
References
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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?
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Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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Pattern Classification and Scene Analysis.
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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.
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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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