Autor: Krawczyk, B - Katalog OPAC zbiorów

Skocz do pozycji: 1.

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

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Skocz do pozycji: 2.

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

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Skocz do pozycji: 3.

Tytuł:: Automatic detection and counting of platelets in microscopic image
Autorzy:: Burduk, R.
Krawczyk, B.
Powiązania:: https://bibliotekanauki.pl/articles/333065.pdf
Data publikacji:: 2010
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie wzorców
nauczanie maszynowe
analiza obrazu
pattern recognition
bioinformatic
machine learning
image analysis
platelet
Opis:: In this paper we present a machine learning-based approach for detecting platelet cells in microscopic smear images. Counting how many platelets appeared in each smear image is one of the basic tasks done in many laboratories. In many cases this is still done by a human — laboratory technician. Due to very small size and often great quantity of those cells, precise estimating of the number of platelets is not a trivial task. As in all man-dependent problems the whole process is very sensitive to errors, time-consuming and its accuracy is limited by human perception. We propose alternative, fully automatic solution that is free of those drawbacks. Our idea is based on the combination of techniques driven from two fields of modern computer science: the image analysis and pattern recognition ⁄ machine learning. It not only reduces the error rate, but, what is more important, also decreases the time needed for each smear image analysis. The obtained results are very satisfying and our solution is more precise than estimation based on human perception. This will improve the quality of laboratory work and allow to save time that can be spent on other important tasks.
Źródło:: Journal of Medical Informatics & Technologies; 2010, 16; 173-178
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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