Peer-reviewed article
Significance of condition attributes in child well-being analysis
E. Adamus ·
Summary
Written by AI, not yet AICS's view · claude-sonnet-5-5This study looks at which survey attributes matter most when evaluating child well-being in Poland. The data came from a survey using a special questionnaire. The authors applied three attribute-ranking filters (χ2, information gain and relief) and one subset-selection method based on rough set theory. The article describes the dataset, names and categorises all attributes, and discusses which attributes were found relevant for child well-being.
Key points
- The main goal was to find the features most specific for evaluating child well-being in Poland.
- Data were collected through a survey based on a special questionnaire.
- Three individual-attribute ranking filters were used: χ2, information gain and relief attribute evaluator.
- One filter-subset selector based on rough set theory was also used.
- All attributes are named, divided by category, and given a domain.
Limitations
The abstract does not state sample size, child ages, or which attributes were found most important. It describes a methodological analysis of survey data, not an intervention, and its findings may not generalise beyond Poland.
Abstract
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 χ2 , 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.