Models of Chance when Measuring Interrater Agreement with Kappa

Two measures of reliability for nominal scales are compared: Coefficient Kappa and k_n, a modification suggested for agreement matrices with free marginals. It is illustrated that the evaluation of two rater's agreement may come to a contradictory conclusion, depending on whether k or k_n is used. On the basis of the underlying chance models it is concluded that k and k_n cannot be interpreted in the same manner. Specifically when raters disagree, the two measures can be widely discrepant.     

Raking Kappa: Describing Potential Impact of Marginal Distributions on Measures of Agreement

Several authors have noted the dependence of kappa measures of inter-rater agreement on the marginal distributions of contingency tables displaying the joint ratings. This paper introduces a smoothed version of kappa computed after raking the table to achieve pre-specified marginal distributions. A comparison of kappa with raked kappa for various margins can indicate the extent of the dependence on the margins, and can indicate how much of the lack of agreement is due to marginal heterogeneity.     

A Ratio Estimator of the Kappa Index of Agreement Between Two Observers

The kappa index is usually used for measuring the agreement between two observers when the scale is nominal. A modification of Cohen's kappa index was given by Krauth. The new estimator was biased and its large sample variance was obtained. An alternative estimator is developed here It is a ratio estimator and its mean square error is derived. A comparison with Cohen's estimator and Krauth's one is given by the examples used in the paper of Krauth.     

A Multiraters Agreement Index for Ordinal Classification

An agreement index among more than two raters who employ ordinal classification is proposed here as an extension of the agreement index set up to consider such agreement between two raters as outlined by (JOLAYEMI , Biom. J. 32 (1990), 87–93). The method of application is outlined using a clinical diagnosis involving seven pathologists.     

Testing the Intraclass Version of Kappa Coeffcient of Agreement with Binary Scale and Sample Size Determination

The intraclass version of kappa coefficient has been commonly applied as a measure of agreement for two ratings per subject with binary outcome in reliability studies. We present an efficient statistic for testing the strength of kappa agreement using likelihood scores, and derive asymptotic power and sample size formula. Exact evaluation shows that the score test is generally conservative and more powerful than a method based on a chi‐square goodness‐of‐fit statistic (Donner and Eliasziw , 1992, Statistics in Medicine 11 , 1511–1519). In particular, when the research question is one directional, the one‐sided score test is substantially more powerful and the reduction in sample size is appreciable.     

Superiority of Classification Tree versus Cluster,Fuzzy and Discriminant Models in a Heartbeat Classification System

This study presents a 2-stage heartbeat classifier of supraventricular (SVB) and ventricular (VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference) beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA) and classification tree (CT), all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features), Fuzzy (72 features), LDA (142 coefficients), CT (221 decision nodes) with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%), LDA (99.6%), Cluster (99.5%), Fuzzy (99.4%); sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies): CT (96.7%), Fuzzy (94.4%), LDA (94.2%), Cluster (92.4%); positive predictivity: CT (99.2%), Cluster (93.6%), LDA (93.0%), Fuzzy (92.4%). CT has superior accuracy by 0.3–6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable ‘if-then’ rules.     

Agreement Between Deoxyribonucleic Acid Base Composition and Taxometric Classification of Gram-Positive Cocci.

Silvestri, L. G. (Università Statale, Milan, Italy), and L. R. Hill. Agreement between deoxyribonucleic acid base composition and taxometric classification of gram-positive cocci. J. Bacteriol. 90:136-140. 1965.-It had been previously proposed, from taxometric analyses, that gram-positive, catalase-positive cocci be divided into two subgroups. Thirteen strains, representative of both subgroups, were examined for deoxyribonucleic acid (DNA) base composition, determined from melting temperatures. Per cent GC (guanine + cytosine/total bases) values fell into two groups: 30.8 to 36.5% GC and 69 to 75% GC. Strains with low per cent GC values belonged to the Staphylococcus aureus-S. saprophyticus-S. lactis taxometric subgroups, and those with high per cent GC values belonged to the S. roseus-S. afermentans subgroup. The hypothetical nature of any classification is emphasized, and, in the present work, the hypothesis derived from taxometric analyses of division into two subgroups is confirmed by the study of DNA base ratios. The two subgroups correspond, respectively, to the genera Staphylococcus and Micrococcus.     

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.     

银杏品种类群的模糊聚类划分

对４２个银杏核用品种的１３个性状特征进行系统聚类分析和主成份分析．根据系统聚类分析树状图和主成份坐标点图,将银杏品种划分为５个主要类群,并对其进行了简要分析。     

Kappa receptor mediated opioid dependence in rhesus monkeys

The kappa receptor-selective agonist U-50, 488 was administered chronically to rhesus monkeys. Tolerance developed to the overt behavioral effects of U-50,488 without cross-tolerance to morphine. Withdrawal behaviors produced by deprivation, naloxone or quadazocine administration in U-50, 488-dependent monkeys consisted of hyperactivity, excessive grooming, and yawning. The syndrome was suppressed in a dose-related manner by a kappa agonist, ethylketazocine, but not by doses of morphine that suppressed its own withdrawal. The mu-selective antagonist, beta-funaltrexamine, at doses which are active in morphine-dependent monkeys, did not precipitate withdrawal in U50, 488-dependent monkeys. Dependence, which is the result of activity at the kappa receptor, was distinct from morphine dependence.     

Speed Constancy or Only Slowness: What Drives the Kappa Effect

In the Kappa effect, two visual stimuli are given, and their spatial distance affects their perceived temporal interval. The classical model assumes constant speed while a competing Bayesian model assumes a slow speed prior. The two models are based on different assumptions about the statistical structure of the environment. Here we introduce a new visual experiment to distinguish between these models. When fit to the data, both the two models replicated human response, but the slowness model makes better behavioral predictions than the speed constancy model, and the estimated constant speed is close to the absolute threshold of speed. Our findings suggest that the Kappa effect appears to be due to slow speeds, and also modulated by spatial variance.     

Hierarchical Gene Selection and Genetic Fuzzy System for Cancer Microarray Data Classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.     

Kappa opioid receptors stimulate phosphoinositide turnover in rat brain

The effects of various subtype-selective opioid agonists and antagonists on the phosphoinositide (PI) turnover response were investigated in the rat brain. The kappa-agonists U-50,488H and ketocyclazocine produced a concentration-dependent increase in the accumulation of IP's in hippocampal slices. The other kappa-agonists Dynorphin-A (1-13) amide, and its protected analog D[Ala]2-dynorphin-A (1-13) amide also produced a significant increase in the formation of [3H]-IP's, whereas the mu-selective agonists [D-Ala2-N-Me-Phe4-Gly5-ol]-enkephalin and morphine and the delta-selective agonist [D-Pen2,5]-enkephalin were ineffective. The increase in IP's formation elicited by U-50,488H was partially antagonized by naloxone and more completely antagonized by the kappa-selective antagonists nor-binaltorphimine and MR 2266. The formation of IP's induced by U-50,488H varies with the regions of the brain used, being highest in hippocampus and amygdala, and lowest in striatum and pons-medulla. The results indicate that brain kappa- but neither mu- nor delta-receptors are coupled to the PI turnover response.