title: Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis.
abstract: Inductive learning systems were successfully applied in a number of medical domains. Nevertheless, the effective use of these systems often requires data preprocessing before applying a learning algorithm. This is especially important for multidimensional heterogeneous data presented by a large number of features of different types. Dimensionality reduction (DR) is one commonly applied approach. The goal of this paper is to study the impact of natural clustering--clustering according to expert domain knowledge--on DR for supervised learning (SL) in the area of antibiotic resistance. We compare several data-mining strategies that apply DR by means of feature extraction or feature selection with subsequent SL on microbiological data. The results of our study show that local DR within natural clusters may result in better representation for SL in comparison with the global DR on the whole data.
authors.affiliation: Department of Computer Science and Information Systems, University of Jyväskylä, Finland. mpechen@cs.jyu.fi
authors.forename: Mykola
authors.initials: M
authors.lastname: Pechenizkiy
authors.forename: Alexey
authors.initials: A
authors.lastname: Tsymbal
authors.forename: Seppo
authors.initials: S
authors.lastname: Puuronen
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D003428:Cross Infection / Q000188:drug therapy* / Q000453:epidemiology*; D016208:Databases, Factual; D024881:Drug Resistance, Bacterial; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*
chemical_list: D000900:Anti-Bacterial Agents