title: Derivation of an artificial gene to improve classification accuracy upon gene selection.
abstract: Classification analysis has been developed continuously since 1936. This research field has advanced as a result of development of classifiers such as KNN, ANN, and SVM, as well as through data preprocessing areas. Feature (gene) selection is required for very high dimensional data such as microarray before classification work. The goal of feature selection is to choose a subset of informative features that reduces processing time and provides higher classification accuracy. In this study, we devised a method of artificial gene making (AGM) for microarray data to improve classification accuracy. Our artificial gene was derived from a whole microarray dataset, and combined with a result of gene selection for classification analysis. We experimentally confirmed a clear improvement of classification accuracy after inserting artificial gene. Our artificial gene worked well for popular feature (gene) selection algorithms and classifiers. The proposed approach can be applied to any type of high dimensional dataset.
authors.affiliation: Department of Nanobiomedical Science, Dankook University, Cheonan 330-714, Republic of Korea. only_love_y@nate.com
authors.forename: Minseok
authors.initials: M
authors.lastname: Seo
authors.forename: Sejong
authors.initials: S
authors.lastname: Oh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000145:classification*; D005813:Genes, Synthetic; D020411:Oligonucleotide Array Sequence Analysis / Q000145:classification*