-Source Statistics to ClassificationErrors.” Journal of Official Statistics 31: 489–506. Doi: http://dx.doi.org/10.1515/jos-2015-0029 . Chipperfield, J. and R. Chambers. 2015. “Using the Bootstrap to Analyse Binary Data Obtained via Probabilistic Linkage.” Journal of Official Statistics 31: 397–414. Doi: http://dx.doi.org/10.1515/JOS-2015-0024 . Christensen, J.L. 2008. “Questioning the Precision of Statistical Classification of Industries.” Paper presented at the DRUID Conference on Entrepreneurship and Innovation, 17–20 June 2008, Copenhagen. Available at: http://www2
Data . Boca Raton, FL: CRC Press. Berzofsky, M.E., P.P. Biemer, and S.L. Edwards. 2015. “Latent Class Analysis with Missing Data under Complex Sampling: Results of a Simulation Study.” Presented at 60th World Statistics Conference, July 26–31, 2015. Rio de Janeiro, Brazil: World Statistics Conference. Berzofsky, M. and P.B. Biemer. 2017. “ClassificationError in Crime Victimization Surveys: A Markov Latent Class Analysis.” In Total Survey Error in Practice , edited by P.P. Biemer, E. de Leeuw, S. Eckman, B. Edwards, F. Kreuter, L.E. Lyberg, N.C. Tucker, and B
National Technical University of Ukraine “KPI” , no. 5, 2014, pp. 55–62.  V. V. Romanuke, “Classificationerror percentage decrement of two-layer perceptron for classifying scaled objects on the pattern of monochrome 60-by-80-images of 26 alphabet letters by training with pixel-distorted scaled images,” Scientific bulletin of Chernivtsi National University of Yuriy Fedkovych. Series: Computer systems and components , vol. 4, iss. 3, 2013, pp. 53–64.  P. A. Castillo, J. J. Merelo, M. G. Arenas, and G. Romero, “Comparing evolutionary hybrid systems for design and
For policymakers and other users of official statistics, it is crucial to distinguish real differences underlying statistical outcomes from noise caused by various error sources in the statistical process. This has become more difficult as official statistics are increasingly based upon a mix of sources that typically do not involve probability sampling. In this article, we apply a resampling method to assess the sensitivity of mixed-source statistics to sourcespecific classification errors. Classification errors can be seen as coverage errors within a stratum. The method can be used to compare relative accuracies between strata and releases, it can assist in deciding how to optimally allocate resources in the statistical process, and it can be applied in evaluating potential estimators. A case study on short-term business statistics shows that bias occurs especially for those strata that deviate strongly from the mean value in other strata. It also suggests that shifting classification resources from small and mediumsized enterprises to large enterprises has virtually no net effect on accuracy, because the gain in precision is offset by the creation of bias. The resampling method can be extended to include other types of nonsampling error.
The aim of this study was to perform and evaluate the accuracy of classification of grains of different cultivars of malting barley. The grains of eight cultivars: Blask, Bor do, Con chita, Kormoran, Mercada, Serwal, Signora, Victoriana, with three moisture content: 12, 14, 16% were examined. The selected parameters of the surface texture of grain mass obtained from images taken using the techniques of hyperspectral imaging were determined. The accuracy of grains discrimination carried out using different methods of selection and classification of data was compared. The pairwise comparison and comparison of three, four and eight cultivars of malting barley were carried out. The most accurate discrimination was determined in the case of the pairwise comparison. Victoriana cultivar was the most different from the others. The most similar texture of grain mass was found in the comparison of cultivars: Blask and Mercada. In the case of eight examined cultivars of malting barley, the most accurate discrimination (classification error – 55%) was obtained for images taken at the moisture content of 14% and at a wavelength of 750 nm, for the attributes selection performed with the use of probability of error and average correlation coefficient (POE+ACC) method and the discrimination carried out using the linear discriminant analysis (LDA).
Both registers and surveys can contain classification errors. These errors can be estimated by making use of a composite data set. We propose a new method based on latent class modelling to estimate the number of classification errors across several sources while taking into account impossible combinations with scores on other variables. Furthermore, the latent class model, by multiply imputing a new variable, enhances the quality of statistics based on the composite data set. The performance of this method is investigated by a simulation study, which shows that whether or not the method can be applied depends on the entropy R2 of the latent class model and the type of analysis a researcher is planning to do. Finally, the method is applied to public data from Statistics Netherlands.
A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification problem. The experimental results revealed that (i) the Bode-Chaudhuri-Hocquenghem (BCH) code matched with any multi-label classifier results in better classification quality; (ii) the accuracy of the binary relevance classification method strongly depends on the coding scheme; (iii) the label power-set and the RAkEL classifier consume the same time for computation irrespective of the coding utilized; (iv) in general, they are not suitable for ECOCs because they are not capable to benefit from ECOC correcting abilities; (v) the all-pairs code combined with binary relevance is not suitable for datasets with larger label sets.
, “Optimizing feedforward artificial neural network architecture,” Engineering Applications of Artificial Intelligence , vol. 20, iss. 3, 2007, pp. 365–382. http://dx.doi.org/10.1016/j.engappai.2006.06.005  V. V. Romanuke, “A 2-layer perceptron performance improvement in classifying 26 turned monochrome 60-by-80-images via training with pixel-distorted turned images,” Research bulletin of the National Technical University of Ukraine “KPI” , no. 5, 2014, pp. 55–62.  V. V. Romanuke, “Classificationerror percentage decrement of two-layer perceptron for classifying
gravitational classification method. We use the general idea of objects’
behavior in a gravity field. Classification depends on a test object’s
motion in a gravity field of training points. To solve this motion problem,
we use a simulation method. This classifier is compared to the 1NN
method, because our method tends towards it for some parameter values.
Experimental results on different data sets demonstrate an improvement
in efficiency and that this approach outperforms the 1NN method by
providing a significant reduction in the mean classificationerror rate.
-to-scale standard deviations ratio optimization for two-layer perceptron training on pixel-distorted scaled 60-by-80-images in scaled objects classification problem,” Visnyk of Kremenchuk National University of Mykhaylo Ostrogradskyy , iss. 2 (85), pp. 96–105, 2014.  V. V. Romanuke, “Classificationerror percentage decrement of two-layer perceptron for classifying scaled objects on the pattern of monochrome 60-by-80-images of 26 alphabet letters by training with pixel-distorted scaled images,” Scientific bulletin of Chernivtsi National University of Yuriy Fedkovych. Series