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Significance of condition attributes in child well-being analysis

Adamus Ewa, Klęsk Przemysław, Kołodziejczyk Joanna, Korzeń Marcin, Piegat Andrzej, Pluciński Marcin

Studia i Materiały Informatyki Stosowanej

2011

nr 4

11--16

In this study the significance of attributes in child well-being is presented. The main goal was to find features most specific for child-well being evaluation in Poland. The dataset was obtained from a survey based on a special questionnaire. To select important attributes three filter for individual attribute rank were used ?2, information gain and relief attribute evaluator and one filter-subset selector based on rough set theory. In the article the dataset is described in details. All the attributes are named, divided by category and for each a domain is given. Then methods of attribute selection applied in experiments are presented. Finally results on selecting attributes relevant for child well-being are discussed.

Application of Rule Induction and Rough Sets to Verification of Magnetic Resonance Diagnosis

Słowiński K., Stefanowski J., Siwiński D.

Fundamenta Informaticae

2002

Vol. 53, nr 3,4

345-363

We discuss a process of analysing medical diagnostic data by means of the combined rule induction and rough set approach. The first step of this analysis includes the use of various techniques for discretization of numerical attributes. Rough sets theory is applied to determine attribute importance for the patients' classification. The novel contribution concerns considering two different algorithms inducing either minimum or satisfactory set of decision rules. Verification of classification abilities of these rule sets is extended by an examination of sensitivity and specificity measures. Moreover, a comparative study of these composed approaches against other learning systems is discussed. The approach is illustrated on a medical problem concerning anterior cruciate ligament (ACL) rupture in a knee. The patients are described by attributes coming from anamnesis, MR examinations and verified by arthroscopy. The clinical impact of our research is indicating two attributes (PCL index, age) and their specific values that could support a physician in resigning from performing arthroscopy for some patients.

Selekcja cech w cyfrowej analizie obrazów biologicznych

Cytowski J., Sakowski T.

Prace Instytutu Podstaw Informatyki Polskiej Akademii Nauk

1998

nr 871

3-32

W pracy przedstawiamy zastosowanie metody decyzyjno-teoretycznrgo rozpoznawania wzorców do analizy obrazów biologicznych. Rozważamy problem wyboru cech obrazów. Na podstawie analizy statystycznej wyznaczamy zbiór cech przydatnych w rozpoznawaniu obrazów biologicznych.

This paper discuss the implementation of a DTPR (decision-theoretical pattern recognition) method for a analysis of a biological images. We discuss in detail the problem of images attributes selection. The paper contains statistical analysis of attributes for biological images recognition.