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Wyświetlanie 1-6 z 6
Tytuł:
Cost-sensitive classifier ensemble for medical decision support system
Autorzy:
Woźniak, M.
Zmyślony, M.
Powiązania:
https://bibliotekanauki.pl/articles/333365.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
klasyfikacja wrażliwa cenowo
systemy wielo-klasyfikatora
rozpoznawanie obrazów
algorytm genetyczny
cost-sensitive classification
multiple classifier systems
pattern recognition
genetic algorithm
Opis:
Multiple classifier systems are currently the focus of intense research. In this conceptual approach, the main effort focuses on establishing decision on the basis of a set of individual classifiers' outputs. This approach is well known but usually most of propositions do not take exploitation cost of such a classifier under consideration. The paper deals with the problem how to take a test acquisition cost during classification task under the framework of combined approach on board. The problem is known as cost-sensitive classification and it has been usually considered for the decision tree induction. In this work we adapt mentioned above idea into choosing members of classifier ensemble and propose a method of choosing a pool of individual classifiers which take into consideration on the one hand quality of ensemble on the other hand cost of classification. Properties of mentioned concept are established during computer experiments conducted on chosen medical benchmark databases from UCI Machine Learning Repository.
Źródło:
Journal of Medical Informatics & Technologies; 2011, 17; 97-104
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Handling class label noise in medical pattern classification systems
Autorzy:
Sáez, J. A.
Krawczyk, B.
Woźniak, M.
Powiązania:
https://bibliotekanauki.pl/articles/333813.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
machine learning
pattern classification
class noise
noise filtering
decision support systems
uczenie maszynowe
klasyfikacja wzorców
filtracja zakłóceń
filtracja szumów
systemy wspomagania decyzji
Opis:
Pattern classification systems play an important role in medical decision support. They allow to automatize and speed-up the data analysis process, while being able to handle complex and massive amounts of information and discover new knowledge. However, their quality is based on the classification models built, which require a training set. In supervised classification we must supply class labels to each training sample, which is usually done by domain experts or some automatic systems. As both of these approaches cannot be deemed as flawless, there is a chance that the dataset is corrupted by class noise. In such a situation, class labels are wrongly assigned to objects, which may negatively affect the classifier training process and impair the classification performance. In this contribution, we analyze the usefulness of existing tools to deal with class noise, known as noise filtering methods, in the context of medical pattern classification. The experiments carried out on several real-world medical datasets prove the importance of noise filtering as a pre-processing step and its beneficial influence on the obtained classification accuracy.
Źródło:
Journal of Medical Informatics & Technologies; 2015, 24; 123-130
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combining classifiers - concept and applications
Autorzy:
Woźniak, M.
Powiązania:
https://bibliotekanauki.pl/articles/333902.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
łączenie klasyfikatorów
systemy wielo-klasyfikatorów
rozpoznawanie obrazów
projekt nagrzewnicy
projekt zespołu
combining classifier
multiple classifier systems
pattern recognition
fuser design
ensemble design
Opis:
Problem of pattern recognition is accompanying our whole life, therefore methods of automatic pattern recognition is one of the main trend in Artificial Intelligence. Multiple classifier systems (MCSs) are currently the focus of intense research. In this conceptual approach, the main effort is concentrated on combining knowledge of the set of individual classifiers. Proposed work presents a brief survey of the main issues connected with MCSs and provides comparative analysis of some classifier fusion methods.
Źródło:
Journal of Medical Informatics & Technologies; 2010, 15; 19-27
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combined classifier based on feature space partitioning
Autorzy:
Woźniak, M.
Krawczyk, B.
Powiązania:
https://bibliotekanauki.pl/articles/331294.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
rozpoznawanie wzorców
system klasyfikujący wielokrotny
algorytm grupowania
algorytm selekcji
algorytm ewolucyjny
pattern recognition
combined classifier
multiple classifier system
clustering algorithm
selection algorithm
evolutionary algorithm
Opis:
This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier, which makes a decision based on a weighted combination of the discriminant functions of the individual classifiers selected for the committee. The weights mentioned above are dependent not only on the classifier identifier, but also on the class number. The proposed approach is based on the results of previous works, where it was proven that such a combined classifier method could achieve significantly better results than simple voting systems. The proposed modification was evaluated through computer experiments, carried out on diverse benchmark datasets. The results are very promising in that they show that, for most of the datasets, the proposed method outperforms similar techniques based on the clustering and selection approach.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 855-866
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hypertension diagnosis using compound pattern recognition methods
Autorzy:
Krawczyk, B.
Woźniak, M.
Powiązania:
https://bibliotekanauki.pl/articles/333544.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
rozpoznawanie wzorców
metody złożone
zespoły klasyfikatora
uczenie maszynowe
niesymetryczna klasyfikacja
uznanie dwustopniowe
pattern recognition
compound methods
classifier ensembles
machine learning
unbalanced classification
two-stage recognition
Opis:
The paper presents a hypertension type classification task where the decisions should be made only on the basis of blood pressure, general information and basis biochemical data. This problem has a great importance to the medical decision support systems, yet results achieved so far are not satisfactory. When the canonical approaches tend to fail we should look for the compound pattern recognition systems, such as multiple classifiers systems. This article presents the results of an experimental investigation of the pool of compound classifiers which have their origin in classifiers ensembles, random forest, and random subspace. Presented methods returned good, satisfactory results, outperforming canonical approaches for this problem.
Źródło:
Journal of Medical Informatics & Technologies; 2011, 18; 41-50
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification techniques for non-invasive recognition of liver fibrosis stage
Autorzy:
Krawczyk, B.
Woźniak, M.
Orczyk, T.
Porwik, P.
Musialik, J.
Błońska-Fajfrowska, B.
Powiązania:
https://bibliotekanauki.pl/articles/332969.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
uczenie maszynowe
systemy wielo-klasyfikatorów
informatyka medyczna
zwłóknienie wątroby
wirusowe zapalenie wątroby typu C
machine learning
multiple classifier systems
compound pattern recognition
medical informatics
liver fibrosis
hepatitis C
Opis:
Contemporary medicine should provide high quality diagnostic services while at the same time remaining as comfortable as possible for a patient. Therefore novel non-invasive disease recognition methods are becoming one of the key issues in the health services domain. Analysis of data from such examinations opens an interdisciplinary bridge between the medical research and artificial intelligence. The paper presents application of machine learning techniques to biomedical data coming from indirect examination method of the liver fibrosis stage. Presented approach is based on a common set of non-invasive blood test results. The performance of four different compound machine learning algorithms, namely Bagging, Boosting, Random Forest and Random Subspaces, is examined and grid search method is used to find the best setting of their parameters. Extensive experimental investigations, carried out on a dataset collected by authors, show that automatic methods achieve a satisfactory level of the fibrosis level recognition and may be used as a real-time medical decision support system for this task.
Źródło:
Journal of Medical Informatics & Technologies; 2012, 20; 121-127
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-6 z 6

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