title: Minimum number of genes for microarray feature selection.
abstract: A fundamental problem in microarray analysis is to identify relevant genes from large amounts of expression data. Feature selection aims at identifying a subset of features for building robust learning models. However, finding the optimal number of features is a challenging problem, as it is a trade off between information loss when pruning excessively and noise increase when pruning is too weak. This paper presents a novel representation of genes as strings of bits and a method which automatically selects the minimum number of genes to reach a good classification accuracy on the training set. Our method first eliminates redundant features, which do not add further information for classification, then it exploits a set covering algorithm. Preliminary experimental results on public datasets confirm the intuition of the proposed method leading to high classification accuracy.
authors.affiliation: Politecnico di Torino, Italy.
authors.forename: Elena
authors.initials: E
authors.lastname: Baralis
authors.forename: Giulia
authors.initials: G
authors.lastname: Bruno
authors.forename: Alessandro
authors.initials: A
authors.lastname: Fiori
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted