scholarly journals Bioassessment of freshwater ecosystems using the Reference Condition Approach: comparing established and new methods with common data sets

2014 ◽  
Vol 33 (4) ◽  
pp. 1204-1211 ◽  
Author(s):  
Robert C. Bailey ◽  
Simon Linke ◽  
Adam G. Yates
Keyword(s):  
Freshwater Ecosystems ◽  
Reference Condition ◽  
Data Sets ◽  
New Methods ◽  
Reference Condition Approach

Considerations when Using the Reference Condition Approach for Bioassessment of Freshwater Ecosystems

2005 ◽  
Vol 40 (3) ◽  
pp. 347-360 ◽  
Author(s):  
Michelle F. Bowman ◽  
Keith M. Somers
Keyword(s):  
Simulated Data ◽  
Test Site ◽  
Variance Partitioning ◽  
Freshwater Ecosystems ◽  
Reference Condition ◽  
Environmental Study ◽  
Reference Sites ◽  
Information Models ◽  
Habitat Gradient ◽  
Reference Condition Approach

Abstract The use of the reference condition approach (RCA) in environmental assessments is becoming more prevalent. Although the RCA was not explicitly described in Green's (1979) book on statistical methods for environmental biologists, we expanded his decision key for selecting an appropriate environmental study design to include this approach. The RCA compares the biological community at a potentially impacted ‘test’ site to communities found in minimally impacted ‘reference’ sites. However, to implement the RCA there are a number of assumptions and decisions that must be made. We compare several common multimetric and multivariate bioassessment methods to illustrate that four key decisions inherent in the RCA framework (i.e., criteria used for reference site selection, for grouping similar reference sites, for comparing test and reference sites, and for evaluating the cause of impacts) can markedly affect test site appraisals. Specific guidelines should be developed to select appropriate reference sites. Based on analyses of real and simulated data, we recommend a minimum of 20, but preferably 30 to 50 reference sites per group, and verification of groupings with more than one classification method. New approaches (e.g., test site analysis) incorporating the strengths of both multimetric and multivariate methods can be used to compare test and reference sites. Additional ecological information, models relating degree of impact to a stressor or habitat gradient, and variance partitioning can also be used to isolate the probable cause of impairment, and are particularly valuable when appropriate reference sites are unavailable.

Evaluating the Type II error rate in a sediment toxicity classification using the Reference Condition Approach

2011 ◽  
Vol 101 (1) ◽  
pp. 207-213 ◽  
Author(s):  
Pilar Rodriguez ◽  
Zuriñe Maestre ◽  
Maite Martinez-Madrid ◽  
Trefor B. Reynoldson
Keyword(s):  
Error Rate ◽  
Sediment Toxicity ◽  
Reference Condition ◽  
Type Ii ◽  
Type Ii Error ◽  
Type Ii Error Rate ◽  
Reference Condition Approach

Mining Environmental Data in the ADMIRE Project Using New Advanced Methods and Tools

2012 ◽  
pp. 296-308
Author(s):  
Ondrej Habala ◽  
Martin Šeleng ◽  
Viet Tran ◽  
Branislav Šimo ◽  
Ladislav Hluchý
Keyword(s):  
Data Mining ◽  
Environmental Data ◽  
Environmental Applications ◽  
Data Sets ◽  
Distributed Data ◽  
New Methods ◽  
Prospective Application ◽  
Using Data ◽  
Computer Power

The project Advanced Data Mining and Integration Research for Europe (ADMIRE) is designing new methods and tools for comfortable mining and integration of large, distributed data sets. One of the prospective application domains for such methods and tools is the environmental applications domain, which often uses various data sets from different vendors where data mining is becoming increasingly popular and more computer power becomes available. The authors present a set of experimental environmental scenarios, and the application of ADMIRE technology in these scenarios. The scenarios try to predict meteorological and hydrological phenomena which currently cannot or are not predicted by using data mining of distributed data sets from several providers in Slovakia. The scenarios have been designed by environmental experts and apart from being used as the testing grounds for the ADMIRE technology; results are of particular interest to experts who have designed them.

scholarly journals Developments in the Calibration and Modeling of Radiocarbon Dates

Radiocarbon ◽  
2010 ◽  
Vol 52 (3) ◽  
pp. 953-961 ◽  
Author(s):  
Christopher Bronk Ramsey ◽  
Michael Dee ◽  
Sharen Lee ◽  
Takeshi Nakagawa ◽  
Richard A Staff
Keyword(s):  
Radiocarbon Dating ◽  
Rapid Development ◽  
Model Averaging ◽  
Data Sets ◽  
Calibration Data ◽  
Statistical Tools ◽  
Core Element ◽  
Radiocarbon Dates ◽  
New Methods ◽  
Age Range

Calibration is a core element of radiocarbon dating and is undergoing rapid development on a number of different fronts. This is most obvious in the area of 14C archives suitable for calibration purposes, which are now demonstrating much greater coherence over the earlier age range of the technique. Of particular significance to this end is the development of purely terrestrial archives such as those from the Lake Suigetsu sedimentary profile and Kauri tree rings from New Zealand, in addition to the groundwater records from speleothems. Equally important, however, is the development of statistical tools that can be used with, and help develop, such calibration data. In the context of sedimentary deposition, age-depth modeling provides a very useful way to analyze series of measurements from cores, with or without the presence of additional varve information. New methods are under development, making use of model averaging, that generate more robust age models. In addition, all calibration requires a coherent approach to outliers, for both single samples and where entire data sets might be offset relative to the calibration curve. This paper looks at current developments in these areas.

Data mining techniques

Acta Numerica ◽  
2001 ◽  
Vol 10 ◽  
pp. 313-355 ◽  
Author(s):  
Markus Hegland
Keyword(s):  
Data Mining ◽  
Efficient Algorithms ◽  
Data Sets ◽  
Mathematical Framework ◽  
Similar Data ◽  
Functional Relationships ◽  
New Methods ◽  
The Common ◽  
Data Objects ◽  
Data Collections

Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Common data mining tasks include the induction of association rules, the discovery of functional relationships (classification and regression) and the exploration of groups of similar data objects in clustering. This review provides a discussion of and pointers to efficient algorithms for the common data mining tasks in a mathematical framework. Because of the size and complexity of the data sets, efficient algorithms and often crude approximations play an important role.

SYNTHETIC OVERSAMPLING OF INSTANCES USING CLUSTERING

2013 ◽  
Vol 22 (02) ◽  
pp. 1350008 ◽  
Author(s):  
ATLÁNTIDA I. SÁNCHEZ ◽  
EDUARDO F. MORALES ◽  
JESUS A. GONZALEZ
Keyword(s):  
Imbalanced Data ◽  
Data Sets ◽  
Minority Class ◽  
Imbalanced Data Sets ◽  
Tuning Parameters ◽  
New Methods ◽  
Real World Applications ◽  
Noisy Examples ◽  
F Measure ◽  
Better Than

Imbalanced data sets in the class distribution is common to many real world applications. As many classifiers tend to degrade their performance over the minority class, several approaches have been proposed to deal with this problem. In this paper, we propose two new cluster-based oversampling methods, SOI-C and SOI-CJ. The proposed methods create clusters from the minority class instances and generate synthetic instances inside those clusters. In contrast with other oversampling methods, the proposed approaches avoid creating new instances in majority class regions. They are more robust to noisy examples (the number of new instances generated per cluster is proportional to the cluster's size). The clusters are automatically generated. Our new methods do not need tuning parameters, and they can deal both with numerical and nominal attributes. The two methods were tested with twenty artificial datasets and twenty three datasets from the UCI Machine Learning repository. For our experiments, we used six classifiers and results were evaluated with recall, precision, F-measure, and AUC measures, which are more suitable for class imbalanced datasets. We performed ANOVA and paired t-tests to show that the proposed methods are competitive and in many cases significantly better than the rest of the oversampling methods used during the comparison.

A Multiclass Classification Method Based on Decoding of Binary Classifiers

2009 ◽  
Vol 21 (7) ◽  
pp. 2049-2081 ◽  
Author(s):  
Takashi Takenouchi ◽  
Shin Ishii
Keyword(s):  
Probabilistic Models ◽  
Experimental Studies ◽  
Synthetic Data ◽  
Multiclass Classification ◽  
Data Sets ◽  
Classification Methods ◽  
Boltzmann Machine ◽  
Data Set ◽  
New Methods ◽  
Binary Classifiers

In this letter, we present new methods of multiclass classification that combine multiple binary classifiers. Misclassification of each binary classifier is formulated as a bit inversion error with probabilistic models by making an analogy to the context of information transmission theory. Dependence between binary classifiers is incorporated into our model, which makes a decoder a type of Boltzmann machine. We performed experimental studies using a synthetic data set, data sets from the UCI repository, and bioinformatics data sets, and the results show that the proposed methods are superior to the existing multiclass classification methods.

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