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    Wyświetlanie 1-13 z 13
    Tytuł:
    Feature Selection and Classification Pairwise Combinations for High-dimensional Tumour Biomedical Datasets
    Autorzy:
    Wosiak, Agnieszka
    Powiązania:
    https://bibliotekanauki.pl/articles/1373672.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
    Tematy:
    feature selection
    classification
    high-dimensional tumour biomedical datasets
    Opis:
    This paper concerns classification of high-dimensional yet small sample size biomedical data and feature selection aimed at reducing dimensionality of the microarray data. The research presents a comparison of pairwise combinations of six classification strategies, including decision trees, logistic model trees, Bayes network, Naive Bayes, k-nearest neighbours and sequential minimal optimization algorithm for training support vector machines, as well as seven attribute selection methods: Correlation-based Feature Selection, chi-squared, information gain, gain ratio, symmetrical uncertainty, ReliefF and SVM-RFE (Support Vector Machine-Recursive Feature Elimination). In this paper, SVMRFE feature selection technique combined with SMO classifier has demonstrated its potential ability to accurately and efficiently classify both binary and multiclass high-dimensional sets of tumour specimens.
    Źródło:
    Schedae Informaticae; 2015, 24; 53-62
    0860-0295
    2083-8476
    Pojawia się w:
    Schedae Informaticae
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The influence of cardiotocogram signal feature selection method on fetal state assessment efficacy
    Autorzy:
    Jeżewski, M.
    Czabański, R.
    Łęski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/333440.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    cardiotocography
    classification
    feature selection
    kardiotokografia
    klasyfikacja
    selekcja cech
    Opis:
    Cardiotocographic (CTG) monitoring is a method of assessing fetal state. Since visual analysis of CTG signal is difficult, methods of automated qualitative fetal state evaluation on the basis of the quantitative description of the signal are applied. The appropriate selection of learning data influences the quality of the fetal state assessment with computational intelligence methods. In the presented work we examined three different feature selection procedures based on: principal components analysis, receiver operating characteristics and guidelines of International Federation of Gynecology and Obstetrics. To investigate their influence on the fetal state assessment quality the benchmark SisPorto® dataset and the Lagrangian support vector machine were used.
    Źródło:
    Journal of Medical Informatics & Technologies; 2014, 23; 51-58
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rough Sets Methods in Feature Reduction and Classification
    Autorzy:
    Świniarski, R. W.
    Powiązania:
    https://bibliotekanauki.pl/articles/908366.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rozpoznawanie obrazów
    redukcja danych
    rough sets
    feature selection
    classification
    Opis:
    The paper presents an application of rough sets and statistical methods to feature reduction and pattern recognition. The presented description of rough sets theory emphasizes the role of rough sets reducts in feature selection and data reduction in pattern recognition. The overview of methods of feature selection emphasizes feature selection criteria, including rough set-based methods. The paper also contains a description of the algorithm for feature selection and reduction based on the rough sets method proposed jointly with Principal Component Analysis. Finally, the paper presents numerical results of face recognition experiments using the learning vector quantization neural network, with feature selection based on the proposed principal components analysis and rough sets methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 565-582
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Feature selection for breast cancer malignancy classification problem
    Autorzy:
    Filipczuk, P.
    Kowal, M.
    Marciniak, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/333614.pdf
    Data publikacji:
    2010
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    wybór funkcji
    klasyfikacja
    rak piersi
    feature selection
    classification
    breast cancer
    Opis:
    The paper provides a preview of some work in progress on the computer system to support breast cancer diagnosis. Diagnosis approach is based on microscope images of the FNB (Fine Needle Biopsy) and assumes distinguishing malignant from benign cases. Studies conducted focus on two different problems, the first concern the extraction of morphometric parameters of nuclei present in cytological images and the other concentrate on breast cancer nature classification using selected features. Studies in both areas are conducted in parallel. This work is devoted to the problem of feature selection from the set of determined features in order to maximize the accuracy of classification. Morphometric features are derived directly from a digital scans of breast fine needle biopsy slides and are computed for segmented nuclei. The quality of feature space is measured with four different classification methods. In order to illustrate the effectiveness of the approach, the automatic system of malignancy classification was applied on a set of medical images with promising results.
    Źródło:
    Journal of Medical Informatics & Technologies; 2010, 15; 193-199
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble of data mining methods for gene ranking
    Autorzy:
    Wiliński, A.
    Osowski, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/201570.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    gene expression array
    feature selection
    gene ranking methods
    classification
    SVM
    Opis:
    The paper presents the ensemble of data mining methods for discovering the most important genes and gene sequences generated by the gene expression arrays, responsible for the recognition of a particular type of cancer. The analyzed methods include the correlation of the feature with a class, application of the statistical hypotheses, the Fisher measure of discrimination and application of the linear Support Vector Machine for characterization of the discrimination ability of the features. In the first step of ranking we apply each method individually, choosing the genes most often selected in the cross validation of the available data set. In the next step we combine the results of different selection methods together and once again choose the genes most frequently appearing in the selected sets. On the basis of this we form the final ranking of the genes. The most important genes form the input information delivered to the Support Vector Machine (SVM) classifier, responsible for the final recognition of tumor from non-tumor data. Different forms of checking the correctness of the proposed ranking procedure have been applied. The first one is relied on mapping the distribution of selected genes on the two-coordinate system formed by two most important principal components of the PCA transformation and applying the cluster quality measures. The other one depicts the results in the graphical form by presenting the gene expressions in the form of pixel intensity for the available data. The final confirmation of the quality of the proposed ranking method are the classification results of recognition of the cancer cases from the non-cancer (normal) ones, performed using the Gaussian kernel SVM. The results of selection of the most significant genes used by the SVM for recognition of the prostate cancer cases from normal cases have confirmed a good accuracy of results. The presented methodology is of potential use for practical application in bioinformatics.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 461-470
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of the recursive feature elimination and the relaxed linear separability feature selection algorithms to gene expression data analysis
    Rekurencyjna eliminacja cech z walidacją oraz relaksacja liniowej separowalności jako metody selekcji cech do analizy zbiorów danych zawierających wartości ekspresji genów
    Autorzy:
    Gościk, J.
    Łukaszuk, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/88402.pdf
    Data publikacji:
    2013
    Wydawca:
    Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
    Tematy:
    gene expression analysis
    feature selection
    classification
    analiza ekspresji genów
    selekcja cech
    klasyfikacja
    Opis:
    Most of the commonly known feature selection methods focus on selecting appropriate predictors for image recognition or generally on data mining issues. In this paper we present a comparison between widely used Recursive Feature Elimination (RFE) with resampling method and the Relaxed Linear Separability (RLS) approach with application to the analysis of the data sets resulting from gene expression experiments. Different types of classification algorithms such as K-Nearest Neighbours (KNN), Support Vector Machines (SVM) and Random Forests (RF) are exploited and compared in terms of classification accuracy with optimal set of genes treated as predictors selected by either the RFE or the RLS approaches. Ten-fold cross-validation was used to determine classification accuracy.
    Zdecydowana większość znanych metod selekcji cech skupia się na wyborze odpowiednich predyktorów dla takich zagadnień jak rozpoznawanie obrazów czy też ogólnie eksploracji danych. W publikacji prezentujemy porównanie pomiędzy powszechnie stosowaną ˛metodą˛ Rekurencyjnej Eliminacji Cech z walidacja˛ (ang. Recursive Feature Elimination - RFE) a metodą stosującą ˛podejście Relaksacji Liniowej Separowalności (ang. Relaxed Linear Separability - RLS) z zastosowaniem do analizy zbiorów danych zawierających wartości ekspresji genów. W artykule wykorzystano różne algorytmy klasyfikacji, takie jak K-Najbliższych Sąsiadów (ang. K-Nearest Neighbours - KNN), Maszynę˛ Wektorów Wspierających (ang. Support Vector Machines - SVM) oraz Lasy Losowe (ang. Random Forests -RF). Porównana została jakość klasyfikacji uzyskana przy pomocy tych algorytmów z optymalnym zestawem cech wygenerowanym z wykorzystaniem metody selekcji cech RFE bądź RLS. W celu wyznaczenia jakości klasyfikacji wykorzystano 10-krotną walidację˛ krzyżową.
    Źródło:
    Advances in Computer Science Research; 2013, 10; 39-52
    2300-715X
    Pojawia się w:
    Advances in Computer Science Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Data mining approach in diagnosis and treatment of chronic kidney disease
    Autorzy:
    Turiac, Andreea S.
    Zdrodowska, Małgorzata
    Powiązania:
    https://bibliotekanauki.pl/articles/2105985.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
    Tematy:
    feature selection
    classification
    classification rules
    action rules
    data mining
    chronic kidney disease
    Opis:
    Chronic kidney disease is a general definition of kidney dysfunction that lasts more than 3 months. When chronic kidney disease is advanced, the kidneys are no longer able to cleanse the blood of toxins and harmful waste products and can no longer support the proper function of other organs. The disease can begin suddenly or develop latently over a long period of time without the presence of characteristic symptoms. The most common causes are other chronic diseases – diabetes and hypertension. Therefore, it is very important to diagnose the disease in early stages and opt for a suitable treatment - medication, diet and exercises to reduce its side effects. The purpose of this paper is to analyse and select those patient characteristics that may influence the prevalence of chronic kidney disease, as well as to extract classification rules and action rules that can be useful to medical professionals to efficiently and accurately diagnose patients with kidney chronic disease. The first step of the study was feature selection and evaluation of its effect on classification results. The study was repeated for four models – containing all available patient data, containing features identified by doctors as major factors in chronic kidney disease, and models containing features selected using Correlation Based Feature Selection and Chi-Square Test. Sequential Minimal Optimization and Multilayer Perceptron had the best performance for all four cases, with an average accuracy of 98.31% for SMO and 98.06% for Multilayer Perceptron, results that were confirmed by taking into consideration the F1-Score, for both algorithms was above 0.98. For all these models the classification rules are extracted. The final step was action rule extraction. The paper shows that appropriate data analysis allows for building models that can support doctors in diagnosing a disease and support their deci-sions on treatment. Action rules can be important guidelines for the doctors. They can reassure the doctor in his diagnosis or indicate new, previously unseen ways to cure the patient.
    Źródło:
    Acta Mechanica et Automatica; 2022, 16, 3; 180--188
    1898-4088
    2300-5319
    Pojawia się w:
    Acta Mechanica et Automatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Wpływ liczby predyktorów na skuteczność algorytmów opartych na drzewach klasyfikacyjnych
    The influence of number of predictors on accuracy of classification algorithms based on trees
    Autorzy:
    Owczarek, T.
    Sojda, A.
    Kaczmarek, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/324994.pdf
    Data publikacji:
    2015
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    klasyfikacja
    dobór zmiennych
    drzewa klasyfikacyjne
    analityka predykcyjna
    classification
    feature selection
    classification trees
    predictive analytics
    Opis:
    Współczesne organizacje, aby być konkurencyjne, muszą mieć umiejętności przetworzenia olbrzymich danych. Jednym z najbardziej obiecujących kierunków w tym zakresie jest wykorzystanie analityki predykcyjnej, opierającej się na algorytmach i modelach uczenia maszynowego. Związanych z tym jest wciąż wiele wyzwań, m.in. pytanie o „wejście” do takich modeli, czy powinny to być wszystkie dane zgromadzone przez organizację czy może raczej wcześniej wybrane zmienne? Celem artykułu jest zbadanie skuteczności algorytmów opartych na drzewach klasyfikacyjnych ze względu na liczebność predyktorów.
    To stay competitive contemporary organizations have to master in processing massive amount of data. Predictive analytics, that is analytics based on machine learning algorithms and models, is one of the most promising directions. But there are many issues involved. One of them is the input to such models: should it be all data gathered by organization or just the selected variables? The aim of the article is to check how the number of predictors influences accuracy of classification algorithms based on trees.
    Źródło:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2015, 86; 507-517
    1641-3466
    Pojawia się w:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Selecting Differentially Expressed Genes for Colon Tumor Classification
    Autorzy:
    Fujarewicz, K.
    Wiench, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/908154.pdf
    Data publikacji:
    2003
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    medycyna
    automatyka
    colon tumor
    gene expression data
    microarrays
    support vector machines
    feature selection
    classification
    Opis:
    DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. Recently we have proposed a new recursive feature replacement (RFR) algorithm for choosing a suboptimal set of genes. The algorithm uses the support vector machines (SVM) technique. In this paper we use the RFR method for finding suboptimal gene subsets for tumor/normal colon tissue classification. The obtained results are compared with the results of applying other methods recently proposed in the literature. The comparison shows that the RFR method is able to find the smallest gene subset (only six genes) that gives no misclassifications in leave-one-out cross-validation for a tumor/normal colon data set. In this sense the RFR algorithm outperforms all other investigated methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2003, 13, 3; 327-335
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Similarity-based methods : a general framework for classification, approximation and association
    Autorzy:
    Duch, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/206007.pdf
    Data publikacji:
    2000
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    aproksymacja
    klasyfikacja
    optymalizacja
    pamięć asocjacyjna
    approximation
    associative memory
    classification
    feature selection
    kNN
    optimization
    similarity-based methods
    Opis:
    Similarity-based methods (SBM) are a generalization of the minimal distance (MD) methods which form a basis of several machine learning and pattern recognition methods. Investigation of similarity leads to a fruitful framework in which many classification, approximation and association methods are accommodated. Probability p(C|X; M) of assigning class C to a vector X, given a classification model M, depends on adaptive parameters and procedures used in construction of the model. Systematic overview of choices available for model building is presented and numerous improvements suggested. Similarity-Based Methods have natural neural-network type realizations. Such neural network models as the Radial Basis Functions (RBF) and the Multilayer Perceptrons (MLPs) are included in this framework as special cases. SBM may also include several different submodels and a procedure to combine their results. Many new versions of similarity-based methods are derived from this framework. A search in the space of all methods belonging to the SBM framework finds a particular combination of parameterizations and procedures that is most appropriate for a given data. No single classification method can beat this approach. Preliminary implementation of SBM elements tested on a real-world datasets gave very good results.
    Źródło:
    Control and Cybernetics; 2000, 29, 4; 937-967
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Electrodermal activity measurements for detection of emotional arousal
    Autorzy:
    Kołodziej, M.
    Tarnowski, P.
    Majkowski, A.
    Rak, R. J.
    Powiązania:
    https://bibliotekanauki.pl/articles/200323.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    electrodermal activity
    EDA
    galvanic skin response
    GSR
    skin conductance response
    SCR
    feature selection
    arousal
    valence
    classification
    regression
    Opis:
    In this article, we present a comprehensive measurement system to determine the level of user emotional arousal by the analysis of electrodermal activity (EDA). A number of EDA measurements were collected, while emotions were elicited using specially selected movie sequences. Data collected from 16 participants of the experiment, in conjunction with those from personal questionnaires, were used to determine a large number of 20 features of the EDA, to assess the emotional state of a user. Feature selection was performed using signal processing and analysis methods, while considering user declarations. The suitability of the designed system for detecting the level of emotional arousal was fully confirmed, throughout the number of experiments. The average classification accuracy for two classes of the least and the most stimulating movies varies within the range of 61‒72%.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 4; 813-826
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Texture-based identification of dystrophy phase. Indicating the most suitable features for therapy testing
    Autorzy:
    Duda, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/333618.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    Golden Retriever Muscular Dystrophy
    MRI-based tissue characterization
    texture analysis
    Monte Carlo feature selection
    classification
    dystrofia mięśniowa Duchenne'a
    obrazowanie metodą rezonansu magnetycznego
    analiza tekstury
    klasyfikacja
    Opis:
    In this study, texture analysis (TA) is applied for characterization of dystrophic muscles visualized on T2-weighted Magnetic Resonance (MR) images. The study proposes a strategy for indicating the textural features that are the most appropriate for testing the therapies of Duchenne muscular dystrophy (DMD). The strategy considers that muscle texture evolves not only along with the disease progression but also with the individual’s development. First, a Monte Carlo (MC) procedure is used to assess the relative importance of each feature in identifying the phases of growth in healthy controls. The features considered as age-dependent at a given acceptance threshold are excluded from further analyses. It is assumed that their application in therapies’ evaluation may entail an incorrect assessment of dystrophy response to treatment. Next, the remaining features are used in differentiation among dystrophy phases. At this step, an MC-based feature selection is applied to find an optimal subset of features. Experiments are repeated at several acceptance thresholds for age-dependent features. Different solutions are finally compared with two classifiers: Neural Network (NN) and Support Vector Machines (SVM). The study is based on the Golden Retriever Muscular Dystrophy (GRMD) model. In total, 39 features provided by 8 TA methods (statistical, filter- and model-based) are tested.
    Źródło:
    Journal of Medical Informatics & Technologies; 2018, 27; 29-40
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A differential evolution approach to dimensionality reduction for classification needs
    Autorzy:
    Martinović, G.
    Bajer, D.
    Zorić, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/331498.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    classification
    differential evolution
    feature subset selection
    k-nearest neighbour algorithm
    wrapper method
    ewolucja różnicowa
    selekcja cech
    algorytm najbliższego sąsiada
    Opis:
    The feature selection problem often occurs in pattern recognition and, more specifically, classification. Although these patterns could contain a large number of features, some of them could prove to be irrelevant, redundant or even detrimental to classification accuracy. Thus, it is important to remove these kinds of features, which in turn leads to problem dimensionality reduction and could eventually improve the classification accuracy. In this paper an approach to dimensionality reduction based on differential evolution which represents a wrapper and explores the solution space is presented. The solutions, subsets of the whole feature set, are evaluated using the k-nearest neighbour algorithm. High quality solutions found during execution of the differential evolution fill the archive. A final solution is obtained by conducting k-fold cross-validation on the archive solutions and selecting the best one. Experimental analysis is conducted on several standard test sets. The classification accuracy of the k-nearest neighbour algorithm using the full feature set and the accuracy of the same algorithm using only the subset provided by the proposed approach and some other optimization algorithms which were used as wrappers are compared. The analysis shows that the proposed approach successfully determines good feature subsets which may increase the classification accuracy.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 111-122
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-13 z 13

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