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Wyszukujesz frazę "reducts" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Partial volume effect detection in MRI segmentation based on approximate decision reducts
Autorzy:
Widz, S.
Powiązania:
https://bibliotekanauki.pl/articles/333876.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
segmentacja obrazów MRI
PVE
zbiory przybliżone
MRI Segmentation
rough sets
approximate decision reducts
Opis:
Segmentation of Magnetic Resonance Imaging (MRI) is a process of assigning tissue class labels to voxels. One of the main sources of segmentation error is the partial volume effect (PVE) which occurs most often with low resolution images - with large voxels, the probability of a voxel containing multiple tissue classes increases. We propose a multistage algorithm for segmenting MRI images with a mid-stage of recognizing the PVE voxels. The information about PVE regions added to other voxels features extracted from the image can increase the overall accuracy of the segmentation. In our methods we have utilize a classification approach based on approximate decision reducts derived from the data mining paradigm of the theory of rough sets. An approximate reduct is an irreducible subset of features, which enables to classify decision concepts with a satisfactory degree of accuracy in the training data. The ensembles of best found reducts trained for appropriate approximation degrees are applied to detection of the PVE and performing the segmentation.
Źródło:
Journal of Medical Informatics & Technologies; 2007, 11; 227-233
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Generation of reducts and rules in multi-attribute and multi-criteria classification
Autorzy:
Susmaga, R.
Słowiński, R.
Greco, S.
Matarazzo, B.
Powiązania:
https://bibliotekanauki.pl/articles/205913.pdf
Data publikacji:
2000
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
decision rules
dominance relation
intelligent information systems
multi-attribute and multi-criteria classification
reducts of attributes and criteria
rough sets theory
Opis:
The paper addresses the problem of analysing information tables which contain objects described by both attributes and criteria, i.e. attributes with preference-ordered scales. The objects contained in those tables, representing exemplary decisions made by a decision maker or a domain expert, are usually classified into one of several classes that are also often preference-ordered. Analysis of such data using the classic rough set methodology may produce improper results, as the original rough set approach is not able to discover inconsistencies originating from consideration of typical criteria, like e.g. product quality, market share or debt ratio. The paper presents the framework for the analysis of both attributes and criteria and a very promising algorithm for generating reducts. The algorithm presented is evaluated in an experiment with real-life data sets and its results are compared to those by two other reduct generating algorithms.
Źródło:
Control and Cybernetics; 2000, 29, 4; 969-988
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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