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About 10,000 papers for “Statistical classification”

statistical classification

M. Doert, M. Errando · 2016

Abstract: The second Fermi-LAT source catalog (2FGL) is the deepest survey of the gamma-ray sky ever compiled, containing 1873 sources that constitute a very complete sample down to an energy flux of ∼ 10−11 erg cm−2 s−1. While counterparts at lower frequencies have been found for a large fraction of 2FGL sources, active galactic nuclei (AGN) being the most numerous class, 576 gamma-ray sources remain unassociated. In these proceedings, we describe a statistical algorithm that finds candidate AGNs in the sample of unassociated 2FGL sources by identifying targets whose gamma-ray properties resemble those of known AGNs. Using two complementary learning algorithms and intersecting the high-probability classifications from both methods, we increase the confidence of the method and reduce the false-association rate to 11%. Our study finds a high-confidence sample of 231 AGN candidates among the population of 2FGL unassociated sources. Selecting sources out of this sample for follow-up observations or studies of archival data will substantially increase the probability to identify possible counterparts at other wavelengths.

Cited by 0

Dynamic statistical classification

Javier Pulido-Cejudo, Carlos Cuevas-Covarrubias · Revista de Matemática Teoría y Aplicaciones · 2017

We consider the statistical supervised classification problem from adynamical systems approach. We assume that two classes exist and that, for each one, a multivariate normal distribution determines the probability to be in a certain region in the n dimensional real vector space. These density functions are the potentials of corresponding gradient vector fields for each class; we construct a “classifying vector field” as a suitable weighted mean ofthem. From data known in the literature, we estimate the population parameters, and the classes are successfully distinguished; we compute and present confusion matrices. A one and two-dimensional analysis is given.

Cited by 0Open access

MKclass: Statistical Classification

Matthias Kohl · 2020

Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991), &lt;<a href="https://doi.org/10.1016%2FB978-1-55860-247-2.50035-8" target="_top">doi:10.1016/B978-1-55860-247-2.50035-8</a>&gt;) for all implemented performance measures. Hosmer-Lemeshow goodness of fit tests (Lemeshow and Hosmer (1982), &lt;<a href="https://doi.org/10.1093%2Foxfordjournals.aje.a113284" target="_top">doi:10.1093/oxfordjournals.aje.a113284</a>&gt;; Hosmer et al (1997), &lt;<a href="https://doi.org/10.1002/(SICI)1097-0258(19970515)16:9%3C965::AID-SIM509%3E3.0.CO;2-O" target="_top">doi:10.1002/(SICI)1097-0258(19970515)16:9%3C965::AID-SIM509%3E3.0.CO;2-O</a>&gt;). Statistical and epidemiological risk measures such as relative risk, odds ratio, number needed to treat (Porta (2014), &lt;<a href="https://doi.org/10.1093%2Facref%2F9780199976720.001.0001" target="_top">doi:10.1093%2Facref%2F9780199976720.001.0001</a>&gt;).

Cited by 1Open access

Statistical Industry Classification

Zura Kakushadze, Willie Yu · RePEc: Research Papers in Economics · 2016

We give complete algorithms and source code for constructing (multilevel) statistical industry classifications, including methods for fixing the number of clusters at each level (and the number of levels). Under the hood there are clustering algorithms (e.g., k-means). However, what should we cluster? Correlations? Returns? The answer turns out to be neither and our backtests suggest that these details make a sizable difference. We also give an algorithm and source code for building "hybrid" industry classifications by improving off-the-shelf "fundamental" industry classifications by applying our statistical industry classification methods to them. The presentation is intended to be pedagogical and geared toward practical applications in quantitative trading.

Cited by 0Open access

Statistical Classification Methods

Barvenčík, Oldřich · Brno University of Technology Digital Library (Brno University of Technology) · 2010

Práce se zabývá vybranými klasifikačními metodami. Jsou zde popsány základy shlukové analýzy, diskriminační analýzy a teorie klasifikačních stromů. Použití metod je ukázáno při klasifikaci simulovaných dat, výpočet je proveden v programu STATISTICA. V praktické části práce pak následuje porovnání metod při klasifikaci reálných datových souborů různých rozsahů. Klasifikačními metodami je také řešena reálná úloha – predikce znečištení ovzduší na základě předpovědi počasí.

Cited by 0

Statistical method of classification

Martinez, Gustavo · Zenodo (CERN European Organization for Nuclear Research) · 2021

Visual representation of the method for statistically classify promoter sequences.

Cited by 0Open access

Statistical classification of images

Giuliodori, María Andrea · Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)

Image classification is a burgeoning field of study. Despite the advances achieved in this camp, there is no general agreement about what is the most effective methods for the classification of digital images. This dissertation contributes to this line of research by developing different statistical methods aim to classifying digital images. In Chapter 1 we introduce basic concepts of image classification and review some results and methodologies proposed previously in the literature. In Chapter 2 we propose a method to classify images by their content. We are able to distinguish between landscape from non-landscape pictures by using three features obtained directly from images. We obtain better classification rates than those obtained by other authors dealing with similar kind of scene classification. In Chapter 3 we address the handwritten digit recognition. We suggest a set of intuitive features to perform the classification. Since the features are calculated with the binary image, we propose a novel technique to obtain the optimum threshold to binarize images, based on statistical concepts associated to the written trace of the digit. The classification is conducted by applying multivariate and probabilistic approaches, concluding that both methods provide similar results in terms of test-error rate (3.5%). In Chapter 4 we propose the application of Functional Data Analysis to analyze and classify images. While a limited number of authors have suggested the application of FDA for image classification [Florindo et al. (2010)], we suggest that this branch of statistics has represents a promising approach and offers several avenues for future research. We close the dissertation in Chapter 5 with a set of concluding remarks. Overall, the methods suggested in this dissertation are simple to apply, intuitive in their interpretation and their performance is comparable with other complex methods applied to the same problem. Moreover, the features suggested require less processing time than other methods (as support vector machine classifiers) and therefore require less computational capacity

Cited by 0Open access