Temat: feature selection - Katalog OPAC zbiorów

Skocz do pozycji: 1.

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

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Skocz do pozycji: 2.

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

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Skocz do pozycji: 3.

Tytuł:: The use of methods of statistical analysis in signature recognition system based on Levenshtein distance
Autorzy:: Pałys, M.
Doroz, R.
Porwik, P.
Powiązania:: https://bibliotekanauki.pl/articles/333624.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie podpisu
znormalizowana odległość Levenshteina
wybór funkcji
signature recognition
normalized Levenshtein distance
feature selection
Opis:: The study being presented is a continuation of the previous studies that consisted in the adaptation and use of the Levenshtein method in a signature recognition process. Three methods based on the normalized Levenshtein measure were taken into consideration. The studies included an analysis and selection of appropriate signature features, on the basis of which the authenticity of a signature was verified later. A statistical apparatus was used to perform a comprehensive analysis. The independence test ◈ was applied. It allowed determining the relationship between signature features and the error returned by the classifier.
Źródło:: Journal of Medical Informatics & Technologies; 2012, 21; 67-73
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Local embedding and dimensionality reduction in detection of skin tumor tissue
Autorzy:: Michalak, M.
Świtoński, A.
Powiązania:: https://bibliotekanauki.pl/articles/333429.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie wzorców
analiza wielospektralna
redukcja wymiarowości
selekcja cech
pattern recognition
multispectral analysis
dimensionality reduction
feature selection
Opis:: This article shows the limitation of the usage of dimensionality reduction methods. For this purpose three algorithms were analyzed on the real medical data. This data are multispectral images of human skin labeled as tumor or non-tumor regions. The classification of new data required the special algorithm of new data mapping that is also described in the paper. Unfortunately, the final conclusion is that this kind of local embedding algorithms should not be recommended for this kind of analysis and prediction.
Źródło:: Journal of Medical Informatics & Technologies; 2012, 19; 59-65
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Selection of the most important components from multispectral images for detection of tumor tissue
Autorzy:: Michalak, M.
Świtoński, A.
Stawarz, M.
Powiązania:: https://bibliotekanauki.pl/articles/951663.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie obrazów
analiza wielospektralna
obniżenie wymiarowości
wybór funkcji
pattern recognition
multispectral analysis
dimensionality reduction
feature selection
Opis:: The problem raised in this article is the selection of the most important components from multispectral images for the purpose of skin tumor tissue detection. It occured that 21 channel spectrum makes it possible to separate healthy and tumor regions almost perfectly. The disadvantage of this method is the duration of single picture acquisition because this process requires to keep the device very stable. In the paper two approaches to the problem are presented: hill climbing strategy and some ranking methods.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 17; 303-308
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: An analysis of a multidimensional dataset of an epidemic study using soft computing tools - a pilot study
Autorzy:: Handri, S.
Nomura, S.
Irfan, A. C.M.
Fukuda, S.
Yamano, E.
Watanabe, Y.
Powiązania:: https://bibliotekanauki.pl/articles/333077.pdf
Data publikacji:: 2009
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: epidemiologia
analiza regresji logistycznej
epidemiology
logistic regression analysis
feature set selection
K-NN analysis
Opis:: Two contrasting approaches toward an epidemic study were illustrated as a pilot study; the regression analysis which is rather conventional methodology used in the past/present epidemic studies, and the other is the classifier analysis which is in the soft computing toolbox. The dataset we used for this study is obtained from a part of a cohort study which principally focused on a fatigue syndrome of the elementary and junior high school educates. In the classifier analysis we employed a major supervised machine-learning algorithm, K-Nearest Neighbour (K-NN), coupled with Principal Component Analysis (PCA). As a result, the performance that was found by cross validation method in the classifier analysis provides better results than that of the regression analysis. Finally we discussed the availability of both analyses with referring the technical and conceptual limitation of both approaches.
Źródło:: Journal of Medical Informatics & Technologies; 2009, 13; 107-110
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: A pattern recognition approach to Emery-Dreifuss muscular dystrophy (EDMD) study
Autorzy:: Sokołowska, B.
Jóźwik, A.
Niebroj-Dobosz, I. M.
Hausmanowa-Petrusewicz, I.
Powiązania:: https://bibliotekanauki.pl/articles/332948.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: pattern recognition
feature selection
pair-wise linear classifier
metalloproteinases and their tissue inhibitors
Emery-Dreifuss muscular dystrophy
rozpoznawanie obrazów
wybór funkcji
metaloproteinaza
dystrofia mięśniowa
Opis:: The algorithms of pattern recognition were used for differentiation between two forms of Emery-Dreifuss muscular dystrophy (EDMD), i.e. autosomal-dominant laminopathy (AD-EDMD) and Xlinked emerynopathy (X-EDMD). A set of some matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs) in serum of EDMD patients and healthy subjects were treated as features. In concluding MMPs and TIMPs levels are helpful to identifying the EDMD patients and the disease progress.
Źródło:: Journal of Medical Informatics & Technologies; 2014, 23; 165-172
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

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

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Skocz do pozycji: 9.

Tytuł:: Some problems with construction of the k-NN classifier for recognition of an experimental respiration pathology
Autorzy:: Jóźwik, A.
Sokołowska, B.
Powiązania:: https://bibliotekanauki.pl/articles/332910.pdf
Data publikacji:: 2002
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie wzorców
klasyfikacja nadzorowana
zasada k-NN
wybór funkcji
oddychanie
wentylacja
paraliż
przepona
pattern recognition
supervised classification
k-NN rule
feature selection
respiration
ventilation
paralysis
diaphragm
Opis:: An objective of the work is to demonstrate some difficulties with construction of a classifier based on the k-NN rule. The standard k-NN classifier and the parallel k-NN classifier have been chosen as the two most powerful approaches. This kind of classifiers has been applied to automatic recognition of diaphragm paralysis degree. The classifier construction consists in determination of the number of nearest neighbors, selection of features and estimation of the classification quality. Three classes of muscle pathology, including the control class, and five ventilatory parameters are taken into account. The data concern a model of the diaphragm pathology in a cat. The animals were forced to breathe in three different experimental situations: air, hypercapnic and hypoxic conditions. A separate classifier is constructed for each kind of the mentioned situations. The calculation of the misclassification rate is based on the leave one out and on the testing set method. Several computational experiments are suggested for the correct feature selection, the classifier type choice and the misclassification probability estimation.
Źródło:: Journal of Medical Informatics & Technologies; 2002, 3; MI89-97
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Nonparametric methods of supervised classification
Autorzy:: Jóźwik, A.
Powiązania:: https://bibliotekanauki.pl/articles/333226.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: pattern recognition
feature selection
k-NN rules
pair-wise classifier
artificial features
linear classifier
reference set size reduction
rozpoznawanie wzorca
wybór funkcji
reguła k-NN
sztuczne cechy
klasyfikator liniowy
Opis:: Selected nonparametric methods of statistical pattern recognition are described. A part of them form modifications of the well known k-NN rule. To this group of the presented methods belong: a fuzzy k-NN rule, a pair-wise k-NN rule and a corrected k-NN rule. They can improve classification quality as compared with the standard k-NN rule. For the cases when these modifications would offer to large error rates an approach based on class areas determination is proposed. The idea of class areas can be also used for construction of the multistage classifier. A separate feature selection can be performed in each stage. The modifications of the k-NN rule and the methods based on determination class areas can be too slow in some applications, therefore algorithms for reference set reduction and condensation, for simple NN rule, are proposed. To construct fast classifiers it is worth to consider also a pair-wise linear classifiers. The presented idea can be used as in the case when the class pairs are linearly separable as well as in the contrary case.
Źródło:: Journal of Medical Informatics & Technologies; 2013, 22; 21-32
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 11.

Tytuł:: The pair-wise linear classifier and the k-NN rule in application to ALS progression differentiation
Autorzy:: Sokołowska, B.
Jóźwik, A.
Niebroj-Dobosz, I.
Janik, P.
Powiązania:: https://bibliotekanauki.pl/articles/333011.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie wzorców
wybór funkcji
klasyfikator liniowy
zasada k-NN
biomarkery
stwardnienie zanikowe boczne
pattern recognition
feature selection
linear classifier
k-NN rule
pair-wise classifier
biomarkers
amyotrophic lateral sclerosis
Opis:: The two kinds of classifier based on the k-NN rule, the standard and the parallel version, were used for recognition of severity of ALS disease. In case of the second classifier version, feature selection was done separately for each pair of classes. The error rate, estimated by the leave one out method, was used as a criterion as for determination the optimum values of k's as well as for feature selection. All features selected in this manner were used in the standard and in the parallel classifier based on k-NN rule. Furthermore, only for the verification purpose, the linear classifier was applied. For this kind of classifier the error rates were calculated by use the training set also as a testing one. The linear classifier was trained by the error correction algorithm with a modified stop condition. The data set concerned with the healthy subjects and patients with amyotrophic lateral sclerosis (ALS). The set of several biomarkers such as erythropoietin, matrix metalloproteinases and their tissue inhibitors measured in serum and cerebrospinal fluid (CSF) were treated as features. It was shown that CSF biomarkers were very sensitive for the ALS progress.
Źródło:: Journal of Medical Informatics & Technologies; 2012, 20; 79-83
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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