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    Wyświetlanie 1-6 z 6
    Tytuł:
    Detection of source code in internet texts using automatically generated machine learning models
    Autorzy:
    Badurowicz, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/2097432.pdf
    Data publikacji:
    2022
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    source code
    binary classification
    text classification
    AutoML
    Opis:
    In the paper, the authors are presenting the outcome of web scraping software allowing for the automated classification of source code. The software system was prepared for a discussion forum for software developers to find fragments of source code that were published without marking them as code snippets. The analyzer software is using a Machine Learning binary classification model for differentiating between a programming language source code and highly technical text about software. The analyzer model was prepared using the AutoML subsystem without human intervention and fine-tuning and its accuracy in a described problem exceeds 95%. The analyzer based on the automatically generated model has been deployed and after the first year of continuous operation, its False Positive Rate is less than 3%. The similar process may be introduced in document management in software development process, where automatic tagging and search for code or pseudo-code may be useful for archiving purposes.
    Źródło:
    Applied Computer Science; 2022, 18, 1; 89--98
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On the binary classification problem in discriminant analysis using linear programming methods
    Autorzy:
    Olusola, Michael O.
    Onyeagu, Sydney I.
    Powiązania:
    https://bibliotekanauki.pl/articles/406591.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    binary classification
    discriminant analysis
    error rate
    hit rate
    linear programming
    Opis:
    This paper is centred on a binary classification problem in which it is desired to assign a new object with multivariate features to one of two distinct populations as based on historical sets of samples from two populations. A linear discriminant analysis framework has been proposed, called the minimised sum of deviations by proportion (MSDP) to model the binary classification problem. In the MSDP formulation, the sum of the proportion of exterior deviations is minimised subject to the group separation constraints, the normalisation constraint, the upper bound constraints on proportions of exterior deviations and the sign unrestriction vis-à-vis the non-negativity constraints. The two-phase method in linear programming is adopted as a solution technique to generate the discriminant function. The decision rule on group-membership prediction is constructed using the apparent error rate. The performance of the MSDP has been compared with some existing linear discriminant models using a previously published dataset on road casualties. The MSDP model was more promising and well suited for the imbalanced dataset on road casualties.
    Źródło:
    Operations Research and Decisions; 2020, 30, 1; 119-130
    2081-8858
    2391-6060
    Pojawia się w:
    Operations Research and Decisions
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of a Function of Misclassified Binary Data
    Autorzy:
    Al-Kandari, Noriah M.
    Powiązania:
    https://bibliotekanauki.pl/articles/973541.pdf
    Data publikacji:
    2016
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    binary classification
    double sampling
    finite population sampling
    misclassification
    linkage error
    sampling design
    Opis:
    We consider the problem of predicting a function of misclassified binary variables. We make an interesting observation that the naive predictor, which ignores the misclassification errors, is unbiased even if the total misclassification error is high as long as the probabilities of false positives and false negatives are identical. Other than this case, the bias of the naive predictor depends on the misclassification distribution and the magnitude of the bias can be high in certain cases. We correct the bias of the naive predictor using a double sampling idea where both inaccurate and accurate measurements are taken on the binary variable for all the units of a sample drawn from the original data using a probability sampling scheme. Using this additional information and design-based sample survey theory, we derive a biascorrected predictor. We examine the cases where the new bias-corrected predictors can also improve over the naive predictor in terms of mean square error (MSE).
    Źródło:
    Statistics in Transition new series; 2016, 17, 3; 429-448
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Emergence of Manichean Political Rhetoric – Theoretical Modeling of Predictive Frameworks
    Autorzy:
    Fontanilla, Edrex
    Juszczak, Mark
    Messina, Rosalie
    Powiązania:
    https://bibliotekanauki.pl/articles/1927350.pdf
    Data publikacji:
    2020
    Wydawca:
    Akademia Pedagogiki Specjalnej im. Marii Grzegorzewskiej. Wydawnictwo APS
    Tematy:
    Manichean rhetoric
    political rhetoric
    binary rhetoric
    complexity theory
    rhetorical classification
    Bourdieu
    Opis:
    Manichean political rhetoric can be best summarized as a generalized trend, by an agent with political power in a given field, to increasingly express themselves in their official capacity as a political actor through a binary lens: presenting issues and/or solutions to the public in that field of power as being either “A or B”. This reductionism in presentation of problems and solutions appears, historically, to coincide with a rise in autocratic behavior on the part of the political actor. To this day, however, a true predictive test for the emergence of Manichean political rhetoric, does not exist. While we can often observe and critic the presence of it, and the transition from complex to binary rhetoric after such rheto-ric has been used, a predictive determinative framework (one that can say with a high degree of accura-cy that this shift is about to happen) still does not exist. This articles is an attempt to do two things: understand more accurately the difficulties that arise in attempting to create such a predictive frame-work and provide theoretical modeling of such frameworks to assess their potential functionality as predictive tools.
    Źródło:
    International Journal of Pedagogy, Innovation and New Technologies; 2020, 7(2); 78-87
    2392-0092
    Pojawia się w:
    International Journal of Pedagogy, Innovation and New Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The application of decision binary trees to assess the usefulness of the digital terrain model in studying the relationships between relief and vegetation in the Polish High Tatra
    Autorzy:
    Kącki, Karol
    Powiązania:
    https://bibliotekanauki.pl/articles/2030343.pdf
    Data publikacji:
    2006-06-01
    Wydawca:
    Uniwersytet Warszawski. Wydział Geografii i Studiów Regionalnych
    Tematy:
    relief - vegetation relationship
    Decision Binary Trees (DBT)
    Digital Terrain Model (DTM)
    Ikonos XS
    image classification
    geoinformation
    Opis:
    The relationships between individual components of the natural environment have long been an object of research (Kostrowicki, Wójcik, 1972; Rączkowska, Kozłowska, 1994; Kozłowska, Rączkowska, 1996). This paper is an attempt to analyse the relationships between two geocomponents of the natural environment: relief and vegetation, from a perspective contrary to the one currently prevailing in the literature of the subject. This approach assumes that relief, with its dominant role as a component strongly affecting the formation of the remaining factors, can be indicative in character and as such can represent basie factors that help determine and anticipate the occurrences of certain plant communities as well as locations with no vegetation. Using geoinformation data along with the tools to process them, an attempt was made to assess the usefulness of the DTM (Digital Terrain Model) to identify selected plant communities, rock and water. The development of a model of the relationships between the relief and the vegetation is an attempt to capture the correspondence between the parameters characterising the relief, calcułated using the DTM model and classes of objects, with the use of information obtained from an Ikonos XS image. This model was subseąuently used to draw a map o f the land cover for a part of the Gąsienicowa Valley in the High Tatra (Dolina Gąsienicowa). For the purpose of this exercise, a techniąue of data classification called DBT (Decision Binary Trees) was used.
    Źródło:
    Miscellanea Geographica. Regional Studies on Development; 2006, 12; 305-313
    0867-6046
    2084-6118
    Pojawia się w:
    Miscellanea Geographica. Regional Studies on Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-label classification using error correcting output codes
    Autorzy:
    Kajdanowicz, T.
    Kazienko, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/331286.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    maszyna ucząca się
    uczenie nadzorowane
    metoda agregacji
    struktura ramowa
    machine learning
    supervised learning
    multilabel classification
    error correcting output codes
    ECOC
    ensemble methods
    binary relevance
    framework
    Opis:
    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.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 829-840
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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