An efficient statistical feature selection approach for classification of gene expression data
B. Chandra,Manish Gupta +1 more
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TLDR
The proposed feature selection algorithm can be helpful in ranking the genes and also is capable of identifying the most relevant genes responsible for diseases like leukemia, colon tumor, lung cancer, diffuse large B-cell lymphoma, prostate cancer.About:
This article is published in Journal of Biomedical Informatics.The article was published on 2011-08-01 and is currently open access. It has received 115 citations till now. The article focuses on the topics: Feature selection & Bayes classifier.read more
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Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection
TL;DR: 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.
Journal ArticleDOI
Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Steven Lawrence Fernandes,Joel En WeiKoh,Edward J. Ciaccio,Mohd Kamil Bin Mohd Fabell,U. John Tanik,Venkatesan Rajinikanth,Chai Hong Yeong +9 more
TL;DR: The findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer’s diagnosis as compared to alternative methods.
Journal ArticleDOI
Deep learning approach for microarray cancer data classification
TL;DR: A deep feedforward method to classify the given microarray cancer data into a set of classes for subsequent diagnosis purposes using a 7-layer deep neural network architecture having various parameters for each dataset is developed.
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Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification
TL;DR: The proposed CBPLR has significant impact in penalized logistic regression by selecting fewer genes with high area under the curve and low misclassification rate, which could conceivably be used in other research that implements gene selection in the field of high dimensional cancer classification.
Journal ArticleDOI
Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering
TL;DR: A hybrid intelligent algorithm, which combines the binary particle swarm optimization (BPSO) with opposition-based learning, chaotic map, fitness based dynamic inertia weight, and mutation, is proposed to solve feature selection problem in the text clustering.
References
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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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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Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
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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.
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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.