A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns.

By Huiqing Liu, Jinyan Li, Limsoon Wong. Genome informatics. International Conference on Genome Informatics, 2002

498 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/83e4b4132b32b58c00e50aedf341626b84ae7ef5

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Citations in the corpus

Feature selection in machine learning: A new perspective (2018, background): “Feature selection, which has been a research topic in methodology and practice for decades, is used in many fields, such as image recognition [10-14], image retrieval [15-17], text mining [18-20], intrusion detection [21-23], bioinformatic data analysis [24-31], fault diagnosis [32-34], and so on.”
A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression (2004, background): “(2002) present a comparative study of various feature selection heuristics using two datasets, Dudoit et al .”
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks (2007, background): “It is a feedforward neural network with two middle layers called radial basis and competitive layers [24], [25].”
A review of feature selection techniques in bioinformatics (2007, methodology): “Different types of population-based randomized heuristics are used as search engines in the major part of these papers: genetic algorithms (Li et al ., 2004; Petricoin et al ., 2002), particle swarm (Bhanot et al ., 2006) Peak Probability Contrast (Tibshirani et al ., 2004) Kolmogorov-Smirnov test (”
Markov blanket-embedded genetic algorithm for gene selection (2007, background): “5, Naive Bayes, etc) on twoclass [9] or multi-class [10,21] microarray classification problems.”
Novel Unsupervised Feature Filtering of Biological Data (2006, unknown): “These algorithms are quite different from the major bulk of feature selection studies that are based on supervised methods (e.g., Guyon and Elissef, 2003, Liu and Wong, 2002), and compared to the latter are relatively overlooked.”
A fast pruned-extreme learning machine for classification problem (2008, background): “Here a hidden node is considered as irrelevant in contributing to the true class label if the relevance measures fall below the normalized relevance threshold parameter, g 2 ½0; 1:0 , which is user-defined.”
Detection of Intrusions and Malware, and Vulnerability Assessment (2013, methodology): “We could also incorporate techniques to reduce the dimensionality in malware clustering [18] and to evaluate malware clustering results using AV labels [30].”
Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases (2011, methodology): “Luukka and Leppälampi (2006) obtained 97.02% using fuzzy similarity classifier for diagnosis of erythemato-squamous diseases. The methods based on fuzzy weighted pre-processing, K-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier for diagnosis of erythemato-squamous d”