A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression

By Tao Li, Chengliang Zhang, Mitsunori Ogihara. Bioinform., 2004

732 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/04a8b53a0395cf554d778b5a77befbe0652faddd

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Citations in the corpus

Markov blanket-embedded genetic algorithm for gene selection (2007, background): “As previous studies have considered no more than 50 and 150 genes as optimal feature subset on two-class datasets [2–5,8,9,14,16,19,20] and multi-class datasets [10,17,18], respectively.”
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework (2007, result): “On the other hand, it has been shown in the literature [8, 24-28 ] that microarray datasets could be learned with high accuracies with only hundreds or tens of features.”
A review of feature selection methods on synthetic data (2013, background): “, a high number of features versus low number of samples; most of these studies also tackle with the multiple class problems [24,35–38].”
Gene selection and classification of microarray data using random forest (2006, unknown): “[8] conducted a comparative study of feature selection and multi-class classification.”
A review of feature selection techniques in bioinformatics (2007, methodology): “Genetic algorithms (Li et al ., 2004; Petricoin et al ., 2002) Nature inspired (Ressom et al ., 2005, 2007) Embedded Random forest/decision tree (Geurts et al ., 2005; Wu et al ., 2003) Weight vector of SVM (Jong et al ., 2004; Prados et al ., 2004; Zhang et al ., 2006) Neural network (Ball et al…”
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes (2011, background): “Their application domain is diverse, for instance, in the field of bioinformatics, classification of microarrays [51] and tissues [71], which operate with several class labels.”
Accurate Cancer Classification Using Expressions of Very Few Genes (2007, unknown): “In [25], Li et al. obtained nearly 70 percent testing accuracy using all 16,063 genes.”
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. (2018, background): “Li et al. (16) compared various state-of-the-art classification methods on numerous multiclass gene expression datasets and found that the multiclass classification problem was much more difficult than the binary one for the gene expression datasets due to the fact that the data are of high…”
An up-to-date comparison of state-of-the-art classification algorithms (2017, background): “Li, Zhang, and Ogihara (2004) provide a comparative study on feature selection and multiclass classiﬁcation for gene expression data.”
A review of microarray datasets and applied feature selection methods (2014, background): “The univariate paradigm, which is fast and scalable but which ignores feature dependencies, has dominated the field during the 2000s [36,74,72].”