Paper Info
Title
Analyzing Discretizations of Continuous Attributes Given a Monotonic Discrimination Function
Abstract
This article addresses the problem of analyzing existing discretizations of continuous attributes with regard to their redundancy and minimality properties. The research was inspired by the increasing number of heuristic algorithms created for generating the discretizations using various methodologies, and apparent lack of any direct techniques for examining the solutions obtained as far as their basic properties, e.g., the redundancy, are concerned. The proposed method of analysis fills this gap by providing a test for redundancy and enabling for a controlled reduction of the discretization's size within specified limits. Rough set theory techniques are used as the basic tools in this method. Exemplary results of discretization analyses for some known real-life data sets are presented for illustration.
Year
DOI
Venue
1997
10.1016/S1088-467X(97)00007-3
Intell. Data Anal.
Keywords
Field
DocType
rough set theory,discretization,decision tables,continuous attributes,heuristic algorithm,decision table,discriminant function
Discretization,Decision table,Computer science,Redundancy (engineering),Artificial intelligence,Monotonic function,Mathematical optimization,Heuristic,Algorithm,Rough set,Seven Basic Tools of Quality,Machine learning,Discretization of continuous features
Journal
Volume
Issue
ISSN
1
3
Intelligent Data Analysis
Citations 
PageRank 
References 
21
2.07
25
Authors
1
Name
Order
Citations
PageRank
Robert Susmaga137033.32