id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_7gj5kurvuzau7m3p6lv7viz5m4	Luís Cavique	A biobjective feature selection algorithm for large omics datasets	2018	23	.pdf	application/pdf	9035	846	62	A Bi-objective Feature Selection Algorithm for Large Omics Datasets attributes, heuristic decomposition uses parallel processing to solve a set The goal of this work is to solve a feature selection problem with a dataset involving of a heuristic decomposition approach with a sub-problem algorithm was presented in the sub-problem algorithm that finds the feature selection with the minimum number of the proposed sub-problem algorithm, two feature selection methods, Rough Sets and into a disjoint matrix and then applies the Set Covering problem to obtain the result. 3. number of features = Minimum Set Covering Problem optimization problem that finds the minimum number of columns/features covering all The sub-problem algorithm returns the pair (number of features, accuracy). of features is given by Algorithm 4, the Set Covering Problem, and the accuracy by the Each sub-problem returns the pair (number of features, accuracy) for the bi-criteria The sub-problem algorithm returns the performance metric (number of features, 	./cache/work_7gj5kurvuzau7m3p6lv7viz5m4.pdf	./txt/work_7gj5kurvuzau7m3p6lv7viz5m4.txt