Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection

By Jun Chin Ang, Andri Mirzal, Habibollah Haron, Haza Nuzly Abdull Hamed. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2016

509 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.

From explanations to feature selection: assessing SHAP values as feature selection mechanism (2020, background): “Feature selection algorithms are usually classified into three groups: filter, wrapper, and embedded [6], [8], [9].”
Feature Selection (2016, background): “…different search strategies such as sequential search (Guyon and Elisseeff 2003), hill-climbing search, best-first search (Kohavi and John 1997; Arai et al. 2016), branch-and-bound search (Narendra and Fukunaga 1977), and genetic algorithms (Golberg 1989) are proposed to yield a local optimum…”
CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research (2019, unknown): “The current scenario is that there are speciﬁc supplementary ﬁles from different works where one may ﬁnd available data sets to test or benchmark ML studies focused on cancer research, but they are majorly scattered through personal, academic, and public repositories.”