An Introspective Comparison of Random Forest-Based Classifiers for the Analysis of Cluster-Correlated Data by Way of RF

• Published in: PloS one Vol. 4; no. 9; p. e7087
• Main Authors: Karpievitch, Yuliya V., Hill, Elizabeth G., Leclerc, Anthony P., Dabney, Alan R., Almeida, Jonas S.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.09.2009; Public Library of Science (PLoS)
• Subjects: Algorithms; Alzheimer's disease; Alzheimers disease; Bioinformatics; Biomarkers; Classification; Cluster Analysis; Clusters; Comparative analysis; Computer Simulation; Correlation analysis; Data mining; Data processing; Decision trees; Downloading; Error correction; Forests; Gene Expression Profiling - methods; Genetics and Genomics/Bioinformatics; Graphical user interface; Indexing; Machine learning; Mass spectrometry; Mass spectroscopy; Mathematics/Statistics; Models, Genetic; Models, Statistical; Molecular Biology/Bioinformatics; Normal distribution; Oligonucleotide Array Sequence Analysis - methods; Ovarian cancer; Pattern Recognition, Automated - methods; Post-processing; Post-production processing; Proteins; Proteomics; Resampling; Sampling methods; Scientific imaging; Software; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods; Statistical methods; Variables; Windows (computer programs); United States > US; Texas; South Carolina

Many mass spectrometry-based studies, as well as other biological experiments produce cluster-correlated data. Failure to account for correlation among observations may result in a classification algorithm overfitting the training data and producing overoptimistic estimated error rates and may make...