Classification Process (classification + process)

Distribution by Scientific Domains

Selected Abstracts

Soil erosion assessment using geomorphological remote sensing techniques: an example from southern Italy

Sergio Lo Curzio
Abstract The aim of this study is to assess of the distribution and map the geomorphological effects of soil erosion at the basin scale identifying newly-formed erosional landsurfaces (NeFELs), by means of an integration of Landsat ETM 7+ remotely sensed data and field-surveyed geomorphological data. The study was performed on a 2286,km2 -wide area, located in southern Italy. The study area was first characterized from a lithological, pedological, land-use and morpho-topographic point of view and thematic maps were created. Then, the georeferenced Landsat ETM 7+ satellite imagery was processed using the RSI ENVI 4.0 software. The processing consisted of contrast stretching, principal component analysis (PCA), decorrelation stretching and RGB false colour compositing. A field survey was conducted to characterize the features detected on the imagery. Particular attention was given to the NeFELs, which were located using a global positioning system (GPS). We then delimited the Regions of Interest (ROI) on the Landsat ETM 7+ imagery, i.e. polygons representing the ,ground-truth', discriminating the NeFELs from the other features occurring in the imagery. A simple statistical analysis was conducted on the digital number (DN) values of the pixels enclosed in the ROI of the NeFELs, with the aim to determine the spectral response pattern of such landsurfaces. The NeFELs were then classified in the entire image using a maximum likelihood classification algorithm. The results of the classification process were checked in the field. Finally, a spatial analysis was performed by converting the detected landsurfaces into vectorial format and importing them into the ESRI ArcViewGIS 9.0 software. Application of these procedures, together with the results of the field survey, highlighted that some ,objects' in the classified imagery, even if displaying the same spectral response of NeFELs, were not landsurfaces subject to intense soil erosion, thus confirming the strategic importance of the field-checking for the automatically produced data. During the production of the map of the NeFELs, which is the final result of the study, these ,objects' were eliminated by means of simple, geomorphologically-coherent intersection procedures in a geographic information system (GIS) environment. The overall surface of the NeFELs had an area of 229,km2, which was 10% of the total. The spatial analysis showed that the highest frequency of the NeFELs occurred on both south-facing and southwest-facing slopes, cut on clayey-marly deposits, on which fine-textured and carbonate-rich Inceptisols were present and displaying slope angle values ranging from 12 to 20. The comparison of two satellite imageries of different periods highlighted that the NeFELs were most clearly evident immediately after summer tillage operations and not so evident before them, suggesting that these practices could have played an important role in inducing the erosional processes. Copyright 2009 John Wiley & Sons, Ltd. [source]

Drug classification: science, politics, both or neither?

ADDICTION, Issue 7 2010
Harold Kalant
ABSTRACT Governments currently classify illicit drugs for various purposes: to guide courts in the sentencing of convicted violators of drug control laws, to prioritize targets of prevention measures and to educate the public about relative risks of the various drugs. It has been proposed that classification should be conducted by scientists and drug experts rather than by politicians, so that it will reflect only accurate factual knowledge of drug effects and risks rather than political biases. Although this is an appealing goal, it is inherently impossible because rank-ordering of the drugs inevitably requires value judgements concerning the different types of harm. Such judgements, even by scientists, depend upon subjective personal criteria and not only upon scientific facts. Moreover, classification that is meant to guide the legal system in controlling dangerous drug use can function only if it is in harmony with the values and sentiments of the public. In some respects, politicians may be better attuned to public attitudes and wishes, and to what policies the public will support, than are scientific experts. The problems inherent in such drug classification are illustrated by the examples of cannabis and of salvinorin A. They raise the question as to whether the classification process really serves any socially beneficial purpose. [source]

Agreement Between Nosologist and Cardiovascular Health Study Review of Deaths: Implications of Coding Differences

Diane G. Ives MPH
OBJECTIVES: To compare nosologist coding of underlying cause of death according to the death certificate with adjudicated cause of death for subjects aged 65 and older in the Cardiovascular Health Study (CHS). DESIGN: Observational. SETTING: Four communities: Forsyth County, North Carolina (Wake Forest University); Sacramento County, California (University of California at Davis); Washington County, Maryland (Johns Hopkins University); and Pittsburgh, Pennsylvania (University of Pittsburgh). PARTICIPANTS: Men and women aged 65 and older participating in CHS, a longitudinal study of coronary heart disease and stroke, who died through June 2004. MEASUREMENTS: The CHS centrally adjudicated underlying cause of death for 3,194 fatal events from June 1989 to June 2004 using medical records, death certificates, proxy interviews, and autopsies, and results were compared with underlying cause of death assigned by a trained nosologist based on death certificate only. RESULTS: Comparison of 3,194 CHS versus nosologist underlying cause of death revealed moderate agreement except for cancer (kappa=0.91, 95% confidence interval (CI)=0.89,0.93). kappas varied according to category (coronary heart disease, kappa=0.61, 95% CI=0.58,0.64; stroke, kappa=0.59, 95% CI=0.54,0.64; chronic obstructive pulmonary disease, kappa=0.58, 95% CI=0.51,0.65; dementia, kappa=0.40, 95% CI=0.34,0.45; and pneumonia, kappa=0.35, 95% CI=0.29,0.42). Differences between CHS and nosologist coding of dementia were found especially in older ages in the sex and race categories. CHS attributed 340 (10.6%) deaths due to dementia, whereas nosologist coding attributed only 113 (3.5%) to dementia as the underlying cause. CONCLUSION: Studies that use only death certificates to determine cause of death may result in misclassification and potential bias. Changing trends in cause-specific mortality in older individuals may be a function of classification process rather than incidence and case fatality. [source]

Adaptive Weighted Learning for Unbalanced Multicategory Classification

BIOMETRICS, Issue 1 2009
Xingye Qiao
Summary In multicategory classification, standard techniques typically treat all classes equally. This treatment can be problematic when the dataset is unbalanced in the sense that certain classes have very small class proportions compared to others. The minority classes may be ignored or discounted during the classification process due to their small proportions. This can be a serious problem if those minority classes are important. In this article, we study the problem of unbalanced classification and propose new criteria to measure classification accuracy. Moreover, we propose three different weighted learning procedures, two one-step weighted procedures, as well as one adaptive weighted procedure. We demonstrate the advantages of the new procedures, using multicategory support vector machines, through simulated and real datasets. Our results indicate that the proposed methodology can handle unbalanced classification problems effectively. [source]