Optimal number of features as a function of sample size for various classification rules

By Jianping Hua, Zixiang Xiong, J. Lowey, E. Suh, Edward R. Dougherty. Bioinform., 2005

451 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets (2020, background): “…on a randomly sampled subset of the images to get scene-level features, (2) projecting them into √ number of samples dimensions (as is recommended in (Hua et al., 2005; Jain and Waller, 1978)) to prevent over-ﬁtting, and then (3) ﬁtting a Linear Support Vector Machine to see if it is able to learn…”
Short-term emotion assessment in a recall paradigm (2009, background): “This confirms the effectiveness of SVM’s in high dimensional spaces (Hua et al., 2005).”
Data Preprocessing for Supervised Leaning (2007, unknown): “class C is higher than the correlation of feature Y with C, then feature X is preferred to Y [20].”
A machine learning-based framework to identify type 2 diabetes through electronic health records (2016, background): “These features are highly correlated with each other, which will influence performances of computational models to do classification [17,19,22].”
Machine learning algorithm validation with a limited sample size (2019, methodology): “This is done because ML algorithms tend to achieve optimal performance in a reduced feature space [6, 16].”
Gene selection and classification of microarray data using random forest (2006, unknown): “Selecting an optimal number of features to use for classification is a complicated task, although some preliminary guidelines, based on simulation studies by [4], are available.”