A call for mtDNA data quality control in forensic science

There is increasing evidence that many of the mitochondrial DNA (mtDNA) databases published in the fields of forensic science and molecular anthropology are flawed. An a posteriori phylogenetic analysis of the sequences could help to eliminate most of the errors and thus greatly improve data quality. However, previously published caveats and recommendations along these lines were not yet picked up by all researchers. Here we call for stringent quality control of mtDNA data by haplogroup-directed database comparisons. We take some problematic databases of East Asian mtDNAs, published in the Journal of Forensic Sciences and Forensic Science International, as examples to demonstrate the process of pinpointing obvious errors. Our results show that data sets are not only notoriously plagued by base shifts and artificial recombination but also by lab-specific phantom mutations, especially in the second hypervariable region (HVR-II). (C) 2003 Elsevier Ireland Ltd. All rights reserved.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.