Wyniki wyszukiwania - BazTech

Ograniczanie wyników

3 Journal of Medical Informatics & Technologies

Znaleziono wyników: 3

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: systemy wielo-klasyfikatorów

Sortuj według:

Ogranicz wyniki do:

On new methods of dynamic ensemble selection based on randomized reference classifier

Krysmann M., Kurzyński M.

Journal of Medical Informatics & Technologies

2012

Vol. 20

101--107

In the paper two dynamic ensemble selection (DES) systems are proposed. Both systems are based on a probabilistic model and utilize the concept of Randomized Reference Classifier (RRC) to determine the competence function of base classifiers. In the first system in the selection procedure of base classifiers the dynamic threshold of competence is applied. In the second DES system, selected classifiers are combined using weighted majority voting rule with continuous-valued outputs, where the weights are equal to the class-dependent competences. The performance of proposed MCSs were tested and compared against DES system with better-than-random selection rule using eleven databases taken from the UCI Machine Learning Repository. The experimental results clearly show the effectiveness of the proposed methods.

Classification techniques for non-invasive recognition of liver fibrosis stage

Krawczyk B., Woźniak M., Orczyk T., Porwik P., Musialik J., Błońska-Fajfrowska B.

Journal of Medical Informatics & Technologies

2012

Vol. 20

121--127

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.

Combining classifiers - concept and applications

Woźniak M.

Journal of Medical Informatics & Technologies

2010

Vol. 15

19--27

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.