title: Information-theoretic approaches to SVM feature selection for metagenome read classification.
abstract: Analysis of DNA sequences isolated directly from the environment, known as metagenomics, produces a large quantity of genome fragments that need to be classified into specific taxa. Most composition-based classification methods use all features instead of a subset of features that may maximize classifier accuracy. We show that feature selection methods can boost performance of taxonomic classifiers. This work proposes three different filter-based feature selection methods that stem from information theory: (1) a technique that combines Kullback-Leibler, Mutual Information, and distance information, (2) a text mining technique, TF-IDF, and (3) minimum redundancy-maximum-relevance (mRMR). The feature selection methods are compared by how well they improve support vector machine classification of genomic reads. Overall, the 6mer mRMR method performs well, especially on the phyla-level. If the number of total features is very large, feature selection becomes difficult because a small subset of features that captures a majority of the data variance is less likely to exist. Therefore, we conclude that there is a trade-off between feature set size and feature selection method to optimize classification performance. For larger feature set sizes, TF-IDF works better for finer-resolutions while mRMR performs the best out of any method for N=6 for all taxonomic levels.
authors.affiliation: Electrical and Computer Engineering Department, Drexel University, 3141 Chestnut St., Philadelphia, PA 19104, USA.
authors.forename: Elaine
authors.initials: E
authors.lastname: Garbarine
authors.forename: Joseph
authors.initials: J
authors.lastname: DePasquale
authors.forename: Vinay
authors.initials: V
authors.lastname: Gadia
authors.forename: Robi
authors.initials: R
authors.lastname: Polikar
authors.forename: Gail
authors.initials: G
authors.lastname: Rosen
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D057225:Data Mining; D030541:Databases, Genetic; D054892:Metagenome / Q000235:genetics*; D017422:Sequence Analysis, DNA