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Journal articles on the topic 'Nested classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Hyun, Baro, Pierre Kabamba, and Anouck Girard. "Optimal Classification by Mixed-Initiative Nested Thresholding." IEEE Transactions on Cybernetics 45, no. 1 (January 2015): 29–39. http://dx.doi.org/10.1109/tcyb.2014.2317672.

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2

Lui, Thomas W. H., and David K. Y. Chiu. "Associative classification using patterns from nested granules." International Journal of Granular Computing, Rough Sets and Intelligent Systems 1, no. 4 (2010): 393. http://dx.doi.org/10.1504/ijgcrsis.2010.036981.

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3

Parvandeh, Saeid, Hung-Wen Yeh, Martin P. Paulus, and Brett A. McKinney. "Consensus features nested cross-validation." Bioinformatics 36, no. 10 (January 27, 2020): 3093–98. http://dx.doi.org/10.1093/bioinformatics/btaa046.

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Abstract Summary Feature selection can improve the accuracy of machine-learning models, but appropriate steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common approach that chooses the classification model and features to represent a given outer fold based on features that give the maximum inner-fold accuracy. Differential privacy is a related technique to avoid overfitting that uses a privacy-preserving noise mechanism to identify features that are stable between training and holdout sets. We develop consensus nested cross-validation (cnCV) that combines the idea of feature stability from differential privacy with nCV. Feature selection is applied in each inner fold and the consensus of top features across folds is used as a measure of feature stability or reliability instead of classification accuracy, which is used in standard nCV. We use simulated data with main effects, correlation and interactions to compare the classification accuracy and feature selection performance of the new cnCV with standard nCV, Elastic Net optimized by cross-validation, differential privacy and private evaporative cooling (pEC). We also compare these methods using real RNA-seq data from a study of major depressive disorder. The cnCV method has similar training and validation accuracy to nCV, but cnCV has much shorter run times because it does not construct classifiers in the inner folds. The cnCV method chooses a more parsimonious set of features with fewer false positives than nCV. The cnCV method has similar accuracy to pEC and cnCV selects stable features between folds without the need to specify a privacy threshold. We show that cnCV is an effective and efficient approach for combining feature selection with classification. Availability and implementation Code available at https://github.com/insilico/cncv. Supplementary information Supplementary data are available at Bioinformatics online.

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Egorov, A. V., M. C. Hansen, D. P. Roy, A. Kommareddy, and P. V. Potapov. "Image interpretation-guided supervised classification using nested segmentation." Remote Sensing of Environment 165 (August 2015): 135–47. http://dx.doi.org/10.1016/j.rse.2015.04.022.

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Al-Debei, Mutaz M., Suzan Tayseer Awienat, and Rana Rasem Abu-Laila. "Nested Circles Boundary Algorithm for Rotated Texture Classification." Journal of Applied Sciences 11, no. 19 (September 15, 2011): 3351–61. http://dx.doi.org/10.3923/jas.2011.3351.3361.

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6

Yang, Gen, Sébastien Destercke, and Marie-Hélène Masson. "Cautious classification with nested dichotomies and imprecise probabilities." Soft Computing 21, no. 24 (July 30, 2016): 7447–62. http://dx.doi.org/10.1007/s00500-016-2287-7.

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7

Pitselis, Georgios. "Multi-stage nested classification credibility quantile regression model." Insurance: Mathematics and Economics 92 (May 2020): 162–76. http://dx.doi.org/10.1016/j.insmatheco.2020.03.007.

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Xia, Jingxin, and Mei Chen. "A Nested Clustering Technique for Freeway Operating Condition Classification." Computer-Aided Civil and Infrastructure Engineering 22, no. 6 (August 2007): 430–37. http://dx.doi.org/10.1111/j.1467-8667.2007.00498.x.

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Pitselis, Georgios. "Quantiles in a multi-stage nested classification credibility model." European Actuarial Journal 10, no. 2 (June 20, 2020): 399–423. http://dx.doi.org/10.1007/s13385-020-00239-w.

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10

Tan, Chuanqi, Wei Qiu, Mosha Chen, Rui Wang, and Fei Huang. "Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9016–23. http://dx.doi.org/10.1609/aaai.v34i05.6434.

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Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework is usually difficult to detect entities with nested structures. The span-based method that can easily detect nested entities in different subsequences is naturally suitable for the nested NER problem. However, previous span-based methods have two main issues. First, classifying all subsequences is computationally expensive and very inefficient at inference. Second, the span-based methods mainly focus on learning span representations but lack of explicit boundary supervision. To tackle the above two issues, we propose a boundary enhanced neural span classification model. In addition to classifying the span, we propose incorporating an additional boundary detection task to predict those words that are boundaries of entities. The two tasks are jointly trained under a multitask learning framework, which enhances the span representation with additional boundary supervision. In addition, the boundary detection model has the ability to generate high-quality candidate spans, which greatly reduces the time complexity during inference. Experiments show that our approach outperforms all existing methods and achieves 85.3, 83.9, and 78.3 scores in terms of F1 on the ACE2004, ACE2005, and GENIA datasets, respectively.

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Filisbino, Tiene A., Gilson A. Giraldi, and Carlos E. Thomaz. "Nested AdaBoost procedure for classification and multi-class nonlinear discriminant analysis." Soft Computing 24, no. 23 (June 12, 2020): 17969–90. http://dx.doi.org/10.1007/s00500-020-05045-w.

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12

Brzeskwiniewicz, Henryk, and Wiesław Wagner. "Error Normality Testing in a Model of Two-Way Nested Classification." Biometrical Journal 36, no. 1 (1994): 87–94. http://dx.doi.org/10.1002/bimj.4710360112.

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13

Miers, Paul. "Typological variation of kinship terminologies is a function of strict ranking of constraints on nested binary classification trees." Behavioral and Brain Sciences 33, no. 5 (October 2010): 395–97. http://dx.doi.org/10.1017/s0140525x10002001.

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AbstractJones argues that extending Seneca kin terms to second cousins requires a revised version of Optimality Theoretic grammar. I extend Seneca terms using three constraints on expression of markers in nested binary classification trees. Multiple constraint rankings on a nested set coupled with local parity checking determines how a given kin classification grammar marks structural endogamy.

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14

Venyo, Anthony Kodzo-Grey. "Nested Variant of Urothelial Carcinoma." Advances in Urology 2014 (2014): 1–24. http://dx.doi.org/10.1155/2014/192720.

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Background. Nested variant of urothelial carcinoma was added to the WHO’s classification in 2004.Aims. To review the literature on nested variant of urothelial carcinoma.Results. About 200 cases of the tumour have been reported so far and it has the ensuing morphological features: large numbers of small confluent irregular nests of bland-appearing, closely packed, haphazardly arranged, and poorly defined urothelial cells infiltrating the lamina propria and the muscularis propria. The tumour has a bland histomorphologic appearance, has an aggressive biological behaviour, and has at times been misdiagnosed as a benign lesion which had led to a significant delay in the establishment of the correct diagnosis and contributing to the advanced stage of the disease. Immunohistochemically, the tumour shares some characteristic features with high-risk conventional urothelial carcinomas such as high proliferation index and loss of p27 expression. However, p53, bcl-2, or EGF-r immunoreactivity is not frequently seen. The tumour must be differentiated from a number of proliferative lesions of the urothelium.Conclusions. Correct and early diagnosis of this tumour is essential to provide early curative treatment to avoid diagnosis at an advanced stage. A multicentre trial is required to identify treatment options that would improve the outcome of this tumour.

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15

Zhong, Yi, Jianghua He, and Prabhakar Chalise. "Nested and Repeated Cross Validation for Classification Model With High-Dimensional Data." Revista Colombiana de Estadística 43, no. 1 (January 1, 2020): 103–25. http://dx.doi.org/10.15446/rce.v43n1.80000.

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With the advent of high throughput technologies, the high-dimensional datasets are increasingly available. This has not only opened up new insight into biological systems but also posed analytical challenges. One important problem is the selection of informative feature-subset and prediction of the future outcome. It is crucial that models are not overfitted and give accurate results with new data. In addition, reliable identification of informative features with high predictive power (feature selection) is of interests in clinical settings. We propose a two-step framework for feature selection and classification model construction, which utilizes a nested and repeated cross-validation method. We evaluated our approach using both simulated data and two publicly available gene expression datasets. The proposed method showed comparatively better predictive accuracy for new cases than the standard cross-validation method.

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16

Willan, Andrew R., William Ross, and Thomas A. Mackenzie. "Comparing in-patient classification systems: A problem of non-nested regression models." Statistics in Medicine 11, no. 10 (1992): 1321–31. http://dx.doi.org/10.1002/sim.4780111006.

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17

Norberg, Ragnar. "Hierarchical credibility: analysis of a random effect linear model with nested classification." Scandinavian Actuarial Journal 1986, no. 3-4 (July 1986): 204–22. http://dx.doi.org/10.1080/03461238.1986.10413807.

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18

Wang, Wei, Li Zhang, Mengjun Zhang, and Zhixiong Wang. "Few shot learning for multi-class classification based on nested ensemble DSVM." Ad Hoc Networks 98 (March 2020): 102055. http://dx.doi.org/10.1016/j.adhoc.2019.102055.

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19

Wagner, Wieslaw, and H. Brzeskwiniewicz. "Normality Testing in a Mixed Model of a Two-way Nested Classification." Biometrical Journal 37, no. 7 (1995): 889–95. http://dx.doi.org/10.1002/bimj.4710370711.

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20

Manica, Denise, Cláudia Schweiger, Leo Sekine, Simone Chaves Fagondes, Gabriel Kuhl, Marcus Vinicius Collares, and Paulo José Cauduro Marostica. "Diagnostic accuracy of current glossoptosis classification systems: A nested cohort cross-sectional study." Laryngoscope 128, no. 2 (September 20, 2017): 502–8. http://dx.doi.org/10.1002/lary.26882.

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21

Adhikari, Uttam, Thomas H. Morris, and Shengyi Pan. "Applying Non-Nested Generalized Exemplars Classification for Cyber-Power Event and Intrusion Detection." IEEE Transactions on Smart Grid 9, no. 5 (September 2018): 3928–41. http://dx.doi.org/10.1109/tsg.2016.2642787.

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22

Anagnostatos, G. S. "FERMION-BOSON CLASSIFICATION IN MICROCLUSTERS." HNPS Proceedings 2 (February 18, 2020): 407. http://dx.doi.org/10.12681/hnps.2867.

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Mlcroclusters composed of atoms with non delocallzed odd number of valence electrons possess the usual magic numbers for fermions in a central potential and those with an even number of valence electrons possess the magic numbers for bosons coming from the packing of atoms in nested icosahedral or octahedral or tetrahedral shells. On the other hand, mlcroclusters composed of atoms with delocallzed valence electrons, either with an odd or with an even number of electrons, exhibit electronic magic numbers (according to the jelllum model) but also magic numbers coming from the (same, as above) packings of their bosonlc ion cores. Finally, through the present work, an alternative approach to study atomic nuclei as quantum clusters appears possible and promising.

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23

Serletis, Apostolos. "A BAYESIAN CLASSIFICATION APPROACH TO MONETARY AGGREGATION." Macroeconomic Dynamics 13, no. 2 (April 2009): 200–219. http://dx.doi.org/10.1017/s1365100508080024.

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In this article we use Bayesian classification and finite mixture models to extract information from the MSI database (maintained by the Federal Reserve Bank of St. Louis) and construct a new set of non-nested monetary aggregates (under the Divisia aggregation procedure) based on statistical similarities and multidimensional structures. We also use recent advances in the fields of applied econometrics, dynamical systems theory, and statistical physics to investigate the relationship between the new money measures and economic activity. The empirical results offer practical evidence in favor of this approach to monetary aggregation.

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24

Cannon, A. J. "Köppen versus the computer: an objective comparison between the Köppen-Geiger climate classification and a multivariate regression tree." Hydrology and Earth System Sciences Discussions 8, no. 2 (March 4, 2011): 2345–72. http://dx.doi.org/10.5194/hessd-8-2345-2011.

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Abstract. A global climate classification is defined using a multivariate regression tree (MRT). The MRT algorithm is automated, which removes the need for a practitioner to manually define the classes; it is hierarchical, which allows a series of nested classes to be defined; and it is rule-based, which allows climate classes to be unambiguously defined and easily interpreted. Climate variables used in the MRT are restricted to those from the Köppen-Geiger climate classification. The result is a hierarchical, rule-based climate classification that can be directly compared against the traditional system. An objective comparison between the two climate classifications at their 5, 13, and 30 class hierarchical levels indicates that both perform well in terms of identifying regions of homogeneous temperature variability, although the MRT still generally outperforms the Köppen-Geiger system. In terms of precipitation discrimination, the Köppen-Geiger classification performs poorly relative to the MRT. The data and algorithm implementation used in this study are freely available. Thus, the MRT climate classification offers instructors and students in the geosciences a simple instrument for exploring modern, computer-based climatological methods.

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25

Qin, Guofeng, Xiaodi Huang, and Yiling Chen. "Nested One-to-One Symmetric Classification Method on a Fuzzy SVM for Moving Vehicles." Symmetry 9, no. 4 (March 26, 2017): 48. http://dx.doi.org/10.3390/sym9040048.

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26

Ren, Jiansi, Ruoxiang Wang, Gang Liu, Yuanni Wang, and Wei Wu. "An SVM-Based Nested Sliding Window Approach for Spectral–Spatial Classification of Hyperspectral Images." Remote Sensing 13, no. 1 (December 31, 2020): 114. http://dx.doi.org/10.3390/rs13010114.

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This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors, which can extract spatial information from the hyperspectral image (HSI) and reconstruct the original data. In the NSW method, the neighbourhood window constructed with the target pixel as the centre contains relevant pixels that are spatially adjacent to the target pixel. In the neighbourhood window, a nested sliding sub-window contains the target pixel and a part of the relevant pixels. The optimal sub-window position is determined according to the average value of the Pearson correlation coefficients of the target pixel and the relevant pixels, and the target pixel can be reconstructed by using the pixels and the corresponding correlation coefficients in the optimal sub-window. By combining NSW with Principal Component Analysis (PCA) and Support Vector Machine (SVM), a classification model, namely NSW-PCA-SVM, is obtained. This paper conducts experiments on three public datasets, and verifies the effectiveness of the proposed model by comparing with two basic models, i.e., SVM and PCA-SVM, and six state-of-the-art models, i.e., CDCT-WF-SVM, CDCT-2DCT-SVM, SDWT-2DWT-SVM, SDWT-WF-SVM, SDWT-2DCT-SVM and Two-Stage. The proposed approach has the following advantages in overall accuracy (OA)—take the experimental results on the Indian Pines dataset as an example: (1) Compared with SVM (OA = 53.29%) and PCA-SVM (OA = 58.44%), NSW-PCA-SVM (OA = 91.40%) effectively utilizes the spatial information of HSI and improves the classification accuracy. (2) The performance of the proposed model is mainly determined by two parameters, i.e., the window size in NSW and the number of principal components in PCA. The two parameters can be adjusted independently, making parameter adjustment more convenient. (3) When the sample size of the training set is small (20 samples per class), the proposed NSW-PCA-SVM approach achieves 2.38–18.40% advantages in OA over the six state-of-the-art models.

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Kizirian, David, and Maureen A. Donnelly. "The criterion of reciprocal monophyly and classification of nested diversity at the species level." Molecular Phylogenetics and Evolution 32, no. 3 (September 2004): 1072–76. http://dx.doi.org/10.1016/j.ympev.2004.05.001.

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Hyung-Jin Lim, Moonseong Kim, Jong-Hyouk Lee, and T. M. Chung. "Route Optimization in Nested NEMO: Classification, Evaluation, and Analysis from NEMO Fringe Stub Perspective." IEEE Transactions on Mobile Computing 8, no. 11 (November 2009): 1554–72. http://dx.doi.org/10.1109/tmc.2009.76.

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Hwang, Yi-Ting, and Peir Feng Wei. "A novel method for testing normality in a mixed model of a nested classification." Computational Statistics & Data Analysis 51, no. 2 (November 2006): 1163–83. http://dx.doi.org/10.1016/j.csda.2005.11.014.

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Melamed, David, and Mike Vuolo. "Assessing Differences between Nested and Cross-Classified Hierarchical Models." Sociological Methodology 49, no. 1 (July 23, 2019): 220–57. http://dx.doi.org/10.1177/0081175019862839.

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In multilevel data, cross-classified data structures are common. For example, this occurs when individuals move to different regions in longitudinal data or students go to different secondary schools than their primary school peers. In both cases, the data structure is no longer fully nested. Estimating cross-classified multilevel models is computationally intensive, so researchers have used several shortcuts to decrease run time. We consider how these shortcuts affect parameter estimates. In particular, we compare parameter estimates from fully nested and cross-classified models using a series of Monte Carlo simulations. When the outcome is continuous, we identify systematic differences in estimated standard errors and some differences in the estimated variance components. When the outcome is binary, we also find differences in the estimated coefficients. Accordingly, we caution researchers to avoid fully nested model specifications when cross-classification exists but suggest some limited conditions under which parameter estimates are unlikely to be different.

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HAO, XIAOLONG, JASON T. L. WANG, MICHAEL P. BIEBER, and PETER A. NG. "HEURISTIC CLASSIFICATION OF OFFICE DOCUMENTS." International Journal on Artificial Intelligence Tools 03, no. 02 (June 1994): 233–65. http://dx.doi.org/10.1142/s0218213094000121.

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Document Processing Systems (DPSs) support office workers to manage information. Document classification is a major function of DPSs. By analyzing a document’s layout and conceptual structures, we present in this paper a sample-based approach to document classification. We represent a document’s layout structure by an ordered labeled tree through a procedure known as nested segmentation and represent the document’s conceptual structure by a set of attribute type pairs. The layout similarities between the document to be classified and sample documents are determined by a previously developed approximate tree matching toolkit. The conceptual similarities between the documents are determined by analyzing their contents and by calculating the degree of conceptual closeness. The document type is identified by computing both the layout and conceptual similarities between the document to be classified and the samples in the document sample base. Some experimental results are presented, which demonstrate the effectiveness of the proposed techniques.

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32

Petscher, Yaacov, and Christopher Schatschneider. "Using n-Level Structural Equation Models for Causal Modeling in Fully Nested, Partially Nested, and Cross-Classified Randomized Controlled Trials." Educational and Psychological Measurement 79, no. 6 (April 9, 2019): 1075–102. http://dx.doi.org/10.1177/0013164419840071.

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Complex data structures are ubiquitous in psychological research, especially in educational settings. In the context of randomized controlled trials, students are nested in classrooms but may be cross-classified by other units, such as small groups. Furthermore, in many cases only some students may be nested within a unit while other students may not. Such instances of partial nesting requires a more flexible framework for estimating treatment effects so that the model coefficients are correctly estimated. Although several recommendations have been offered to the field on handling partially nested data, few are comprehensive in their treatment of manifest and latent variables in the context of partial nesting, full nesting, and cross-classification. The present study introduces n-level structural equation modeling (SEM) as a flexible measurement and analytic framework for the estimation of treatment effects for complex data structures that frequently present in randomized controlled trials. In this tutorial, we explore how the notation of n-level SEM allows for parsimonious model specification whether data are observed or latent and in the presence of partial nested or cross-classified designs. By using the xxm package in R, the advantage of using n-level SEM framework is demonstrated through five examples for single outcome manifest variables, as in the traditional multilevel model, as well as latent applications as in multilevel SEM.

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Morton, Lindsay M., Jennifer J. Turner, James R. Cerhan, Martha S. Linet, Patrick A. Treseler, Christina A. Clarke, Andrew Jack, et al. "Proposed classification of lymphoid neoplasms for epidemiologic research from the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph)." Blood 110, no. 2 (July 15, 2007): 695–708. http://dx.doi.org/10.1182/blood-2006-11-051672.

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Abstract Recent evidence suggests that there is etiologic heterogeneity among the various subtypes of lymphoid neoplasms. However, epidemiologic analyses by disease subtype have proven challenging due to the numerous clinical and pathologic schemes used to classify lymphomas and lymphoid leukemias over the last several decades. On behalf of the International Lymphoma Epidemiology Consortium (InterLymph) Pathology Working Group, we present a proposed nested classification of lymphoid neoplasms to facilitate the analysis of lymphoid neoplasm subtypes in epidemiologic research. The proposed classification is based on the World Health Organization classification of lymphoid neoplasms and the International Classification of Diseases–Oncology, Third Edition (ICD-O-3). We also provide a translation into the proposed classification from previous classifications, including the Working Formulation, Revised European-American Lymphoma (REAL) classification, and ICD-O-2. We recommend that epidemiologic studies include analyses by lymphoma subtype to the most detailed extent allowable by sample size. The standardization of groupings for epidemiologic research of lymphoma subtypes is essential for comparing subtype-specific reports in the literature, harmonizing cases within a single study diagnosed using different systems, as well as combining data from multiple studies for the purpose of pooled analysis or meta-analysis, and will probably prove to be critical for elucidating etiologies of the various lymphoid neoplasms.

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Budijaja, K. Y., and K. C. Bhuyan. "On Covariance Analysis in Nested Classification to Study the Fertility Differential in North-Eastern Libya." Calcutta Statistical Association Bulletin 49, no. 1-2 (March 1999): 101–10. http://dx.doi.org/10.1177/0008068319990110.

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35

Takhvar, Mehri, and Peter K. Smith. "A Review and Critique of Smilansky's Classification Scheme and the “Nested Hierarchy” of Play Categories." Journal of Research in Childhood Education 4, no. 2 (June 1990): 112–22. http://dx.doi.org/10.1080/02568549009594792.

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36

Jamalabadi, Hamidreza, Sarah Alizadeh, Monika Schönauer, Christian Leibold, and Steffen Gais. "Multivariate classification of neuroimaging data with nested subclasses: Biased accuracy and implications for hypothesis testing." PLOS Computational Biology 14, no. 9 (September 27, 2018): e1006486. http://dx.doi.org/10.1371/journal.pcbi.1006486.

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37

Verschae, Rodrigo, Javier Ruiz-del-Solar, and Mauricio Correa. "A unified learning framework for object detection and classification using nested cascades of boosted classifiers." Machine Vision and Applications 19, no. 2 (October 9, 2007): 85–103. http://dx.doi.org/10.1007/s00138-007-0084-0.

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38

Dora, Lingraj, Sanjay Agrawal, Rutuparna Panda, and Ajith Abraham. "Nested cross-validation based adaptive sparse representation algorithm and its application to pathological brain classification." Expert Systems with Applications 114 (December 2018): 313–21. http://dx.doi.org/10.1016/j.eswa.2018.07.039.

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Spang, R., and F. Markowetz. "Molecular Diagnosis." Methods of Information in Medicine 44, no. 03 (2005): 438–43. http://dx.doi.org/10.1055/s-0038-1633990.

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Summary Objectives: We discuss supervised classification techniques applied to medical diagnosis based on gene expression profiles. Our focus lies on strategies of adaptive model selection to avoid overfitting in high-dimensional spaces. Methods: We introduce likelihood-based methods, classification trees, support vector machines and regularized binary regression. For regularization by dimension reduction, we describe feature selection methods: feature filtering, feature shrinkage and wrapper approaches. In small sample-size situations efficient methods of data re-use are needed to assess the predictive power of a model. We discuss two issues in using cross-validation: the difference between in-loop and out-of-loop feature selection, and estimating model parameters in nested-loop cross-validation. Results: Gene selection does not reduce the dimensionality of the model. Tuning parameters enable adaptive model selection. The feature selection bias is a common pitfall in performance evaluation. Model selection and performance evaluation can be combined by nested-loop cross-validation. Conclusions: Classification of microarrays is prone to overfitting. A rigorous and unbiased assessment of the predictive power of the model is a must.

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Hayes, J. J. C., E. Kerins, S. Awiphan, I. McDonald, J. S. Morgan, P. Chuanraksasat, S. Komonjinda, N. Sanguansak, and P. Kittara. "Optimizing exoplanet atmosphere retrieval using unsupervised machine-learning classification." Monthly Notices of the Royal Astronomical Society 494, no. 3 (April 14, 2020): 4492–508. http://dx.doi.org/10.1093/mnras/staa978.

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ABSTRACT One of the principal bottlenecks to atmosphere characterization in the era of all-sky surveys is the availability of fast, autonomous, and robust atmospheric retrieval methods. We present a new approach using unsupervised machine learning to generate informed priors for retrieval of exoplanetary atmosphere parameters from transmission spectra. We use principal component analysis (PCA) to efficiently compress the information content of a library of transmission spectra forward models generated using the platon package. We then apply a k-means clustering algorithm in PCA space to segregate the library into discrete classes. We show that our classifier is almost always able to instantaneously place a previously unseen spectrum into the correct class, for low-to-moderate spectral resolutions, R, in the range R = 30−300 and noise levels up to 10 per cent of the peak-to-trough spectrum amplitude. The distribution of physical parameters for all members of the class therefore provides an informed prior for standard retrieval methods such as nested sampling. We benchmark our informed-prior approach against a standard uniform-prior nested sampler, finding that our approach is up to a factor of 2 faster, with negligible reduction in accuracy. We demonstrate the application of this method to existing and near-future observatories, and show that it is suitable for real-world application. Our general approach is not specific to transmission spectroscopy and should be more widely applicable to cases that involve the repetitive fitting of trusted high-dimensional models to large data catalogues, including beyond exoplanetary science.

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Adler, W., A. Peters, and B. Lausen. "Comparison of Classifiers Applied to Confocal Scanning Laser Ophthalmoscopy Data." Methods of Information in Medicine 47, no. 01 (2008): 38–46. http://dx.doi.org/10.3414/me0348.

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Summary Objectives: Comparison of classification methods using data of one clinical study. The tuning of hyperparameters is assessed as part of the methods by nested-loop cross-validation. Methods: We assess the ability of 18 statistical and machine learning classifiers to detect glaucoma. The training data set is one case-control study consisting of confocal scanning laser ophthalmoscopy measurement values from 98 glaucoma patients and 98 healthy controls. We compare bootstrap estimates of the classification error by the Wilcoxon signed rank test and box-plots of a bootstrap distribution of the estimate. Results: The comparison of out-of-bag bootstrap estimators of classification errors is assessed by Spearman’s rank correlation, Wilcoxon signed rank tests and box-plots of a bootstrap distribution of the estimate. The classification methods random forests 15.4%, support vector machines 15.9%, bundling 16.3% to 17.8%, and penalized discriminant analysis 16.8% show the best results. Conclusions: Using nested-loop cross-validation we account for the tuning of hyperparameters and demonstrate the assessment of different classifiers. We recommend a block design of the bootstrap simulation to allow a statistical assessment of the bootstrap estimates of the misclassification error. The results depend on the data of the clinical study and the given size of the bootstrap sample.

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Moerbeek, Mirjam, and Maryam Safarkhani. "The Design of Cluster Randomized Trials With Random Cross-Classifications." Journal of Educational and Behavioral Statistics 43, no. 2 (September 26, 2017): 159–81. http://dx.doi.org/10.3102/1076998617730303.

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Data from cluster randomized trials do not always have a pure hierarchical structure. For instance, students are nested within schools that may be crossed by neighborhoods, and soldiers are nested within army units that may be crossed by mental health–care professionals. It is important that the random cross-classification is taken into account while planning a cluster randomized trial. This article presents sample size equations, such that a desired power level is achieved for the test on treatment effect. Furthermore, it also presents optimal sample sizes given a budgetary constraint, with a special focus on conditional optimal designs where one of the sample sizes is fixed beforehand. The optimal design methodology is illustrated using a postdeployment training to reduce ill-health in armed forces personnel.

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Stein, J. L., M. F. Hutchinson, and J. A. Stein. "A new stream and nested catchment framework for Australia." Hydrology and Earth System Sciences 18, no. 5 (May 22, 2014): 1917–33. http://dx.doi.org/10.5194/hess-18-1917-2014.

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Abstract. Nationally framed assessment and planning assists coordination of resource management activities across jurisdictional boundaries and provides context for assessing the cumulative effects of impacts that can be underestimated by local or regional studies. However, there have been significant shortcomings in the existing spatial frameworks supporting national assessment and planning for Australia's rivers and streams. We describe the development of a new national stream and nested catchment framework for Australia that includes a fully connected and directed stream network and a nested catchment hierarchy derived using a modified Pfafstetter scheme. The directed stream network with associated catchment boundaries and Pfafstetter coding respect all distributary junctions and topographically driven surface flow pathways, including those in the areas of low relief and internal drainage that make up over half of the Australian continent. The Pfafstetter coding facilitates multi-scale analyses and easy tracing and query of upstream/downstream attributes and tributary/main stem relationships. Accompanying the spatial layers are 13 lookup tables containing nearly 400 attributes describing the natural and anthropogenic environment of each of the 1.4 M stream segments at multiple spatial scales (segment, sub-catchment and catchment). The database supplies key spatial layers to support national water information and accounting needs and assists a wide range of research, planning and assessment tasks at regional and continental scales. These include the delineation of reporting units for the Australian Water Resources Assessment, the development of an ecohydrological environment classification for Australian streams and the identification of high conservation value aquatic ecosystems for northern Australia.

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Stein, J. L., M. F. Hutchinson, and J. A. Stein. "A new stream and nested catchment framework for Australia." Hydrology and Earth System Sciences Discussions 10, no. 12 (December 17, 2013): 15433–74. http://dx.doi.org/10.5194/hessd-10-15433-2013.

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Abstract. Nationally framed assessment and planning assists coordination of resource management activities across jurisdictional boundaries and provides context for assessing the cumulative effects of impacts that can be underestimated by local or regional studies. However, there were significant shortcomings in the existing spatial frameworks supporting national assessment and planning for Australia's rivers and streams. We describe the development of a new national stream and nested catchment framework for Australia that includes a fully connected and directed stream network and a nested catchment hierarchy derived using a modified Pfafstetter scheme. The directed stream network with associated catchment boundaries and Pfafstetter coding respect all distributary junctions and topographically driven surface flow pathways including across the areas of low relief and internal drainage that make up over half of the Australian continent. The Pfafstetter coding facilitates multi-scale analyses and easy tracing and query of upstream/downstream attributes and tributary/main stem relationships. Accompanying the spatial layers are 13 lookup tables containing nearly 400 attributes describing the natural and anthropogenic environment of each of the 1.4M stream segments across the Australian continent at multiple spatial scales (segment, sub-catchment and catchment). The database supplies key spatial layers to support national water information and accounting needs and assists a wide range of research, planning and assessment tasks at regional and continental scales. These include the delineation of reporting units for the Australian Water Resources Assessment, the development of an ecohydrological environment classification for Australian streams and the identification of high conservation value aquatic ecosystems for northern Australia.

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Burdick, Richard K., and Franklin A. Graybill. "Confidence intervals on the total variance in an unbalanced two-fold nested classification with equal subsampling." Communications in Statistics - Theory and Methods 14, no. 4 (January 1985): 761–74. http://dx.doi.org/10.1080/03610928508828948.

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Adhikari, Kaushallya, and Benjamin Drozdenko. "Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal Classification." IEEE Access 7 (2019): 153217–29. http://dx.doi.org/10.1109/access.2019.2948503.

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ZENG, WENYI, YU SHI, and HONGXING LI. "REPRESENTATION THEOREM OF INTERVAL-VALUED FUZZY SET." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 03 (June 2006): 259–69. http://dx.doi.org/10.1142/s0218488506003996.

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In this paper, we introduce the concept of interval-valued nested set on the universal set X, propose two representation theorems and equivalent classification theorem of interval-valued fuzzy set. These works can be used in setting up the basic theory of interval-valued fuzzy set.

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Lal, Kishan, Rajender Prasad, and V. K. Gupta. "Trend‐Free Nested Balanced Incomplete Block Designs and Designs for Diallel Cross Experiments." Calcutta Statistical Association Bulletin 59, no. 3-4 (September 2007): 203–21. http://dx.doi.org/10.1177/0008068320070306.

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Abstract: Nested balanced incomplete block (NBIB) designs are useful when the experiments are conducted to deal with experimental situations when one nuisance factor is nested within the blocking factor. Similar to block designs, trend may exist in experimental units within sub‐blocks or within blocks in NBIB designs over time or space. A necessary and sufficient condition, for a nested block design to be trend‐free at sub‐block level, is derived. Families and catalogues of NBIB designs that can be converted into trend‐free NBIB designs at sub‐block and block levels have been obtained. A NBIB design with sub‐block size 2 has a one to one correspondence with designs for diallel crosses experiments. Therefore, optimal block designs for dialled cross experiments have been identified to check if these can be converted in to trend‐free optimal block designs for diallel cross experiments. A catalogue of such designs is also obtained. Trend‐free design is illustrated with example for a NBIB design and a design for diallel crosses experiments. AMS (2000) Subject Classification: 62K05, 62K10.

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Hsu, Chin-Wang, Jen-Chun Wang, Wen-I. Liao, Wu-Chien Chien, Chi-Hsiang Chung, Chang-Huei Tsao, Yung-Fu Wu, Min-Tser Liao, and Shih-Hung Tsai. "Association between malignancies and Marfan syndrome: a population-based, nested case–control study in Taiwan." BMJ Open 7, no. 10 (October 2017): e017243. http://dx.doi.org/10.1136/bmjopen-2017-017243.

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ObjectiveMarfan syndrome (MFS) involves a deficiency of the structural extracellular matrix component fibrillin-1 and overactivation of the transforming growth factor-β (TGF-β) signalling pathway. The TGF-β signalling pathway also actively participates in malignant transformation. Although anecdotal case reports have suggested associations between MFS/MFS-like conditions and several haematological and solid malignancies, such associations have not been thoroughly evaluated in large-scale studies. We sought to use a nationwide healthcare insurance claim database to evaluate whether patients with MFS are at increased risk of malignancy.Patients and methodsWe conducted a nested case–control analysis using a database extracted from Taiwan’s National Health Insurance Research Database. All medical conditions for each case and control were categorised using the International Classification of Diseases, 9th Revision classifications. ORs and 95% CIs for associations between MFS and malignancies were estimated using conditional logistic regression and adjusted for comorbidities.ResultsOur analyses included 1 153 137 cancer cases and 1 153 137 propensity score-matched controls. Relative to other subjects, patients with MFS had a significantly higher risk of having a malignancy (adjusted OR 3.991) and hypertension (adjusted OR 1.964) and were significantly more likely to be men. Malignancies originating from the head and neck and the urinary tract were significantly more frequent among patients with MFS than among subjects without MFS.ConclusionPatients with MFS are at increased risk of developing various malignancies. Healthcare professionals should be aware of this risk when treating such patients, and increased cancer surveillance may be necessary for these patients.

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Quinn, Thomas, Daniel Tylee, and Stephen Glatt. "exprso: an R-package for the rapid implementation of machine learning algorithms." F1000Research 5 (December 6, 2017): 2588. http://dx.doi.org/10.12688/f1000research.9893.2.

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Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce exprso, a new R package that is an intuitive machine learning suite designed specifically for non-expert programmers. Built initially for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso also supports multi-class classification (through the 1-vs-all generalization of binary classifiers) and the prediction of continuous outcomes.

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