Abstract
Feature selection is an important process in machine learning. It aims to identify relevant features that describe the problem in the context of the categories distinguished in the problem domain. This paper presents the results of a study on the feasibility of using a discretization and value fuzzification process for numerical attributes to identify significant ranges of attribute values. In medicine it is applicable to identify significant values of parameters, medical indicators so that the size of medical examinations can be reduced and appropriate interpretation of symptoms can be created. The results obtained show that this is possible without losing the quality of classification while reducing the number of attributes. Moreover, by analyzing the obtained value intervals, it is possible to detail the feature selection process and detect irrelevant features not previously identified.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
