Temat: fuzzy classification - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A new approach for the clustering using pairs of prototypes
Autorzy:: Jezewski, M.
Czabanski, R.
Leski, J.
Horoba, K.
Powiązania:: https://bibliotekanauki.pl/articles/333693.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: fuzzy clustering
pairs of prototypes
fuzzy rule-based classification
grupowanie rozmyte
pary prototypów
rozmyta klasyfikacja oparta na regułach
Opis:: In the presented work two variants of the fuzzy clustering approach dedicated for determining the antecedents of the rules of the fuzzy rule-based classifier were presented. The main idea consists in adding additional prototypes (’prototypes in between’) to the ones previously obtained using the fuzzy c-means method (ordinary prototypes). The ’prototypes in between’ are determined using pairs of the ordinary prototypes, and the algorithm based on distances and densities finding such pairs was proposed. The classification accuracy obtained applying the presented clustering approaches was verified using six benchmark datasets and compared with two reference methods.
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 113-121
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Application of modified fuzzy clustering to medical data classification
Autorzy:: Jeżewski, M.
Powiązania:: https://bibliotekanauki.pl/articles/333509.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: grupowanie rozmyte
klasyfikacja
dane medyczne
fuzzy clustering
classification
medical data
Opis:: Classification plays very important role in medical diagnosis. This paper presents fuzzy clustering method dedicated to classification algorithms. It focuses on two additional sub-methods modifying obtained clustering prototypes and leading to final prototypes, which are used for creating the classifier fuzzy if-then rules. The main goal of that work was to examine a performance of the classifier which uses such rules. Commonly used including medical benchmark databases were applied. In order to validate the results, each database was represented by 100 pairs of learning and testing subsets. The obtained classification quality was better in relation to the one of the best classifiers - Lagrangian SVM and suggests that presented clustering with additional sub-methods are appropriate to application to classification algorithms.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 17; 51-57
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: An approach to unsupervised classification
Autorzy:: Przybyła, T.
Pander, T.
Horoba, K.
Kupka, T.
Matonia, A.
Powiązania:: https://bibliotekanauki.pl/articles/333363.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: klasyfikacja
grupowanie rozmyte
klasyfikacja nienadzorowana
klasyfikator najbliższych sąsiadów
classification
fuzzy clustering
unsupervised classification
nearest neighbour classifier
Opis:: Classification methods can be divided into supervised and unsupervised methods. The supervised classifier requires a training set for the classifier parameter estimation. In the case of absence of a training set, the popular classifiers (e.g. K-Nearest Neighbors) can not be used. The clustering methods are considered as unsupervised classification methods. This paper presents an idea of the unsupervised classification with the popular classifiers. The fuzzy clustering method is used to create a learning set. The learning set includes only these patterns that are the best representative of each class in the input dataset. The numerical experiment uses an artificial dataset as well as the medical datasets (PIMA, Wisconsin Breast Cancer) and illustrates the usefulness of the proposed method.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 17; 105-111
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Medical diagnosis using fuzzy cognitive map classifier
Autorzy:: Froelich, W.
Wrobel, K.
Powiązania:: https://bibliotekanauki.pl/articles/333970.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: fuzzy cognitive map
medical diagnosis
classification
rozmyta mapa poznawcza
diagnostyka medyczna
klasyfikacja
Opis:: In this study, we address the problem of medical diagnosis by applying Fuzzy Cognitive Map (FCM). A distinctive feature of the FCM is its ability to simulate the development of the disease in time. By this simulation, it is possible to predict the severity of the disease by having future knowledge on current medical investigations. For the first time in this paper, we construct an FCM-based classifier dedicated solely to perform medical diagnosis. To learn the FCM, we use an evolutionary algorithm explicitly specifying the newly designed fitness function. Real, publicly available medical data are applied for the validation and evaluation of the proposed approach.
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 247-254
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Fuzzy system for evaluation of fetal heart rate signals using FIGO criteria
Autorzy:: Czabański, R.
Jeżewski, M.
Wróbel, J.
Jeżewski, J.
Horoba, K.
Powiązania:: https://bibliotekanauki.pl/articles/333142.pdf
Data publikacji:: 2009
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: monitoring płodu
tętno płodu
kryteria FIGO
fetal monitoring
fetal heart rate
signal classification
fuzzy systems
Opis:: Cardiotocography is a biophysical method of fetal monitoring during pregnancy and labour. It is mainly based on recording and analysis of fetal heart activity. The computerized fetal monitoring systems provide the quantitative description of the recorded signals but the effective methods supporting the conclusion generation are still needed. The evaluation of the signal can be made using criteria recommended by FIGO. Nevertheless, the quantitative description of the traces is inconsistent with qualitative nature of the obstetric knowledge. Therefore, we applied the fuzzy system based on Takagi-Sugeno-Kang model to evaluate and classify signals. FIGO guidelines were used for developing a set of fuzzy conditional rules defining the system performance. The proposed system was evaluated using data collected with computerized fetal surveillance system – MONAKO. The classification results confirm the improvement of the fetal state evaluation quality while using the proposed fuzzy system support.
Źródło:: Journal of Medical Informatics & Technologies; 2009, 13; 189-194
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Fuzzy prediction of fetal acidemia
Autorzy:: Czabański, R.
Roj, D.
Jeżewski, J.
Horoba, K.
Jeżewski, M.
Powiązania:: https://bibliotekanauki.pl/articles/333483.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: monitorowanie płodu
tętno płodu
klasyfikacja sygnału
systemy rozmyte
fetal monitoring
fetal heart rate
signal classification
fuzzy systems
Opis:: Cardiotocography is the primary method for biophysical assessment of a fetal state. It is based mainly on the recording and analysis of fetal heart rate signal (FHR). Computer systems for fetal monitoring provide a quantitative description of FHR signals, however the effective methods for their qualitative assessment are still needed. The measurements of hydronium ions concentration (pH) in newborn cord blood is considered as the objective indicator of the fetal state. Improper pH level is a symptom of acidemia being the result of fetal hypoxia. The paper proposes a twostep analysis of signals allowing for effective prediction of the acidemia risk. The first step consists in the fuzzy classification of FHR signals. The task of fuzzy inference is to indicate signals that according to the FIGO guidelines represent the fetal wellbeing. These recordings are eliminated from the further classification with Lagrangian Support Vector Machines. The proposed procedure was evaluated using data collected with computerized fetal surveillance system. The classification results confirmed the high quality of the proposed fuzzy method of fetal state evaluation.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 17; 81-87
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Unsupervised clustering for fetal state assessment based on selected features of the cardiotocographic signals
Autorzy:: Przybyła, T.
Jeżewski, J.
Roj, D.
Powiązania:: https://bibliotekanauki.pl/articles/333112.pdf
Data publikacji:: 2009
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: klasyfikacja algorytmów bez nadzoru
grupowanie danych
monitoring płodu
kardiotokografia
unsupervised classification
fuzzy clustering
principal component analysis
fetal monitoring
Opis:: In modern obstetrics the cardiotocography is a routine method of fetal condition assessment based mainly on analysis of the fetal heart rate signals. The correct interpretation of recorded traces from a bedside monitor is very difficult even for experienced clinicians. Therefore, computerized fetal monitoring systems are used to yield the quantitative description of the signal. However, the effective techniques enabling automated conclusion generation based on cardiotocograms are still being searched. The paper presents an attempt to diagnose the fetal state basing on seventeen features describing the cardiotocographic records. The proposed method applies the unsupervised classification of signals. During our research we tried to classify the fetal state using the fuzzy c-means (FCM) clustering. We also tested how the efficiency of classification could be influenced by application of principal component analysis (PCA) algorithm. The obtained results showed that unsupervised classification cannot be considered as a support to fetal state assessment.
Źródło:: Journal of Medical Informatics & Technologies; 2009, 13; 157-162
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: The neurological disease classification by means of its single descriptors coverage finding
Autorzy:: Piecha, J.
Zyguła, J.
Powiązania:: https://bibliotekanauki.pl/articles/333946.pdf
Data publikacji:: 2007
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: wnioski automatyczne
medyczne systemy ekspertowe
choroby neurologiczne
decyzje rozmyte
czynniki klasyfikacji chorób
automatic conclusions
medical expert systems
neurological diseases
fuzzy decisions
disease classification factors
Opis:: The reported diagnosis supporting system was provided with knowledge base determined by the disease characteristic features descriptors that were recorded in conclusions table. Every descriptor defines the elementary rules related to every disease factor threshold value, recognised as a sign of the disease presence (the over-gone physiological state). The introduced definitions of the disease characteristics and some fuzzy logic proposals implementations were defined for the decision making system development.
Źródło:: Journal of Medical Informatics & Technologies; 2007, 11; 311-319
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Patient classification algorithm at urgency care area of a hospital based on the triage system
Autorzy:: Mondragon, N.
Istrate, D.
Wegrzyn-Wolska, K.
Garcia, J. C.
Sanchez, J.C.
Powiązania:: https://bibliotekanauki.pl/articles/951692.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: triage
classification
SET
fuzzy logic
decision trees
patients
urgency
hospital emergency
algorithm
ocena stanu zdrowia rannych
klasyfikacja
logika rozmyta
drzewa decyzyjne
pacjenci
pomoc szpitalna
algorytm
Opis:: The time passed in the urgency zone of a hospital is really important, and the quick evaluation and selection of the patients who arrive to this area is essential to avoid waste of time and help the patients in a higher emergency level. The triage, an evaluation and classification structured system, allows to manage the urgency level of the patient; it is based on the vital signs measures and clinical data of the patient. The goal is making the classification in the shortest possible time and with a minimal error percentage. Levels are allocated according to the concept that what is urgent is not always serious and that what is serious is not always urgent. In this work, we present a computational algorithm that evaluates the patients within the fever symptomatic category, we use fuzzy logic and decision trees to collect and analyze simultaneously the vital signs and the clinical data of the patient through a graphical interface; so that the classification can be more intuitive and faster. Fuzzy logic allows us to process data and take a decision based on incomplete information or uncertain values, decision trees are structures or rules sets that classify the data when we have several variables.
Źródło:: Journal of Medical Informatics & Technologies; 2013, 22; 87-94
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: New frontiers of analysis, interpretation and classification of biomedical signals: a computational intelligence framework
Autorzy:: Gacek, A.
Powiązania:: https://bibliotekanauki.pl/articles/333497.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: sygnał EKG
inteligencja obliczeniowa
zbiory rozmyte
granulki informacji
ziarnista informatyka
interpretacja
klasyfikacja
współdziałanie
ECG signals
computational intelligence
neurocomputing
fuzzy sets
information granules
granular computing
interpretation
classification
interpretability
Opis:: The methods of Computational Intelligence (CI) including a framework of Granular Computing, open promising research avenues in the realm of processing, analysis and interpretation of biomedical signals. Similarly, they augment the existing plethora of "classic" techniques of signal processing. CI comes as a highly synergistic environment in which learning abilities, knowledge representation, and global optimization mechanisms and this essential feature is of paramount interest when processing biomedical signals. We discuss the main technologies of Computational Intelligence (namely, neural networks, fuzzy sets, and evolutionary optimization), identify their focal points and elaborate on possible limitations, and stress an overall synergistic character, which ultimately gives rise to the highly symbiotic CI environment. The direct impact of the CI technology on ECG signal processing and classification is studied with a discussion on the main directions present in the literature. The design of information granules is elaborated on; their design realized on a basis of numeric data as well as pieces of domain knowledge is considered. Examples of the CI-based ECG signal processing problems are presented. We show how the concepts and algorithms of CI augment the existing classification methods used so far in the domain of ECG signal processing. A detailed construction of granular prototypes of ECG signals being more in rapport with the diversity of signals analyzed is discussed as well. ECG signals, Computational Intelligence, neurocomputing, fuzzy sets, information granules, Granular Computing, interpretation, classification, interpretability.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 17; 23-36
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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