Abstract
Objectives: Knowledge Discovery methods get more accurate results when the dimensionality of the data is subsided; dimensionality is an important aspect of any data. Several algorithms have been proposed to increase the accuracy, but most of them generate complex models as the size of the data is extremely large. Objective of this paper is to build a simple model to get high accuracy. Method: In order to increase the accuracy of the Knowledge Discovery methods by substituting the dimensionality, we introduce a novel heuristic functionality, Arbitrary Gini Index (ArGI). Findings: We evaluated the performance of ArGI on the real world datasets. The experiment on the ten real world data sets analysis shows 60% data sets are more accurate for ArGI and 40% for Gini Index. Applications: It is expecting that the applications of ArGI will show a better approach in the real world learning tasks.
Sign-in/Register to access full text options 
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.
