Feature selection for high-dimensional genomic microarray data

By Eric P. Xing, Michael I. Jordan, Richard M. Karp. 2001

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Citations in the corpus

Feature selection for text classification: A review (2018, methodology): “Most hybrid methods employ some sort of filter methods to select promising features at first, then apply wrapper methods to the obtained features, for example those methods in [104] and [68], which are designed for generic data though instead of for text corpus.”
mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification (2011, background): “In the classification phase, a point x is assigned a label y according to y = sgn[w x + b].”
Gene selection from microarray data for cancer classification - a machine learning approach (2005, methodology): “Most methods of gene selection for microarray data analysis focus on filter approaches, although there are a few publications on applying wrapper approaches (Inza et al., 2004; Xiong et al., 2001; Xing et al., 2001) .”
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy (2003, background): “…recognition applications, identifying the most characterizing features (or attributes) of the observed data, i.e., feature selection (or variable selection, among many other names) [30], [14], [17], [18], [15], [12], [11], [19], [31], [32], [5], is critical to minimize the classification error.”
Efficient Feature Selection via Analysis of Relevance and Redundancy (2004, background): “…applications like text categorization (Yang and Pederson, 1997; Forman, 2003), image retrieval (Swets and Weng, 1995; Dy et al., 2003), genomic microarray analysis (Xing et al., 2001; Yu and Liu, 2004), customer relationship management (Ng and Liu, 2000), and intrusion detection (Lee et al., 2000).”
Feature Selection: A literature Review (2014, background): “In the application perspective, many real-world applications like intrusion detection [100, 101], text categorization [95,102, 109, 110], DNA microarray analysis [103], music information retrieval [104], image retrieval [105], information retrieval [106], customer relationship management [107], Geno”
Incremental wrapper-based gene selection from microarray data for cancer classification (2006, unknown): “Yu and Liu (2004a) proposed a fast correlation–based filter algorithm (FCBF) which used correlation measure to obtain relevant genes and to remove redundancy.”
A review of feature selection techniques in bioinformatics (2007, methodology): “…2001; Pan, 2003; Park et al ., 2001; Tusher et al. , 2001) USC (Yeung and Bumgarner, 2003) Markov blanket (Gevaert et al ., 2006; Mamitsuka, 2006; Xing et al ., 2001) Gamma (Newton et al ., 2001) TNoM (Ben-Dor et al ., 2000) (cid:1) the gene ranking output could fulfill the objectives and…”
Feature Selection With Harmony Search (2012, background): “Human evaluation and subsequent pattern identification are limited when considering data sets which have very large numbers of features [46], [57].”
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution (2003, methodology): “in a hybrid model have recently been proposed to deal with high dimensional data (Das, 2001; Ng, 1998; Xing et al., 2001).”
Redundancy based feature selection for microarray data (2004, methodology): “Therefore, selecting a small number of discriminative genes from thousands of genes is essential for successful sample classification [5, 7, 22].”
A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data (2015, methodology): “The following methods were proposed in order to rank the genes in a dataset based on their significance [12]: (i) (Univariate) Unconditional Mixture Modelling assumes two different states of the gene on and off and checks whether the underlying binary state of the gene affects the classification…”
A Survey of Transfer and Multitask Learning in Bioinformatics (2011, background): “This makes it difficult to apply traditional feature selection approaches directly to this data to reduce its dimensionality [15].”