Cohen’s quadratically weighted kappa is higher than linearly weighted kappa for tridiagonal agreement tables

Warrens, Matthijs J.

doi:10.1016/j.stamet.2011.08.006

Cited by 17 publications

(14 citation statements)

References 26 publications

(42 reference statements)

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“…If is 0, the agreement is equal to that expected under independence. A negative value indicates that the agreement is less than expected by chance [27]. But finally, the interpretation of the magnitude of is arbitrary.…”

Section: Statistical Evaluation Of Empirical Classification Resultsmentioning

confidence: 95%

Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations

Breker¹,

Claudi

Sick

2015

IEEE Trans. Power Syst.

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Without appropriate counteraction, the high amount of installed distributed generators (DG) at the low-voltage distribution level may cause overloading of electrical equipments and violation of voltage limits in many grids. Because of the historically grown low-voltage grids and their local and geographic dependencies, complex grid structures can be found. Thus, the discrimination of grids concerning their DG capacity is a difficult task. We propose a novel three-step classification strategy to distinguish various kinds of low-voltage grids regarding their DG capacity. Our method is based on a stochastic simulation procedure and a subsequent parametric stochastic modeling, which allows for a probability based classification approach. The classification results can be regarded as probabilistic class memberships or, if sharp memberships are required, the class with the maximum probability can be selected. The proposed approach will not only help distribution system operators to face the challenges in future grid planning and focus their work on further enhancement of weak grid structures, but it will also be valuable in choosing relevant grids for detailed surveys. To demonstrate that our approach actually leads to meaningful classification results for real low-voltage grids, we empirically evaluate the results for 300 real rural and suburban grids by comparing them to classification assessments of experts from distribution grid planning practice.

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Section: Statistical Evaluation Of Empirical Classification Resultsmentioning

confidence: 95%

Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations

Breker¹,

Claudi

Sick

2015

IEEE Trans. Power Syst.

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“…If κ w is 0, the agreement is equal to that expected under independence. A negative value indicates that the agreement is less than expected by chance [20].…”

Section: Properties Of the Combination Rulesmentioning

confidence: 94%

Combination of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM

Breker

Sick

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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The use of expert knowledge is always more or less afflicted with uncertainties for many reasons: Expert knowledge may be imprecise, imperfect, or erroneous, for instance. If we ask several experts to label data (e.g., to assign class labels to given data objects, i.e. samples), we often state that these experts make different, sometimes conflicting statements. The problem of labeling data for classification tasks is a serious one in many technical applications where it is rather easy to gather unlabeled data, but the task of labeling requires substantial effort regarding time and, consequently, money. In this article, we address the problem of combining several, potentially wrong class labels. We assume that we have an ordinal class structure (i.e., three or more classes are arranged such as "light", "medium-weight", and "heavy") and only a few expert statements are available. We propose a novel combination rule, the Extended Imprecise Dirichlet Model Rule (EIDMR) which is based on a k-nearestneighbor approach and Dirichlet distributions, i.e., second-order distributions for multinomial distributions. In addition, experts may assess the difficulty of the labeling task, which may optionally be considered in the combination. The combination rule EIDMR is compared to others such as a standard Imprecise Dirichlet Model Rule, the Dempster-Shafer Rule, and Murphy's Rule. In our evaluation of EIDMR we first use artificial data where we know the data characteristics and true class labels. Then, we present results of a case study where we classify lowvoltage grids with Support Vector Machines (SVM). Here, the task is to assess the expandability of these grids with additional photovoltaic generators (or other distributed generators) by assigning these grids to one of five ordinal classes. It can be shown that the use of our new EIDMR leads to better classifiers in cases where ordinal class labels are used and only a few, uncertain expert statements are available.

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“…Ä w , however, is more appropriate when not all disagreements among raters are equally important as, for example, in the case of models that predict an ordinal variable (Cohen, 1968;Fleiss and Cohen, 1973;Warrens, 2011Warrens, , 2012. As the target variable of the developed BBN (BMWP/Col index) is ordinal, Ä w is more suitable as model performance evaluator for this model.…”

Section: Model Validationmentioning

confidence: 99%

Bayesian belief network models to analyse and predict ecological water quality in rivers

Forio

Landuyt

Bennetsen

et al. 2015

Ecological Modelling

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Cohen’s quadratically weighted kappa is higher than linearly weighted kappa for tridiagonal agreement tables

Cited by 17 publications

References 26 publications

Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations

Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations

Combination of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM

Bayesian belief network models to analyse and predict ecological water quality in rivers

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