Paper Info
Title
Lazy Induction Of Descriptions Using Two Fuzzy Versions Of The Rand Index
Abstract
In this paper we introduce an extension of the lazy learning method called Lazy Induction of Descriptions (LID). This new version is able to deal with fuzzy cases, i.e., cases described by attributes taking continuous values represented as fuzzy sets. LID classifies new cases based on the relevance of the attributes describing them. This relevance is assessed using a distance measure that compares the correct partition (i.e., the correct classification of cases) with the partitions induced by each one of the attributes. The fuzzy version of LID introduced in this paper uses two fuzzy versions of the Rand index to compare fuzzy partitions: one proposed by Campello and another proposed by Hullermeier and Rifqi. We experimented with both indexes on data sets from the UCI machine learning repository.
Year
DOI
Venue
2010
10.1007/978-3-642-14055-6_41
INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: THEORY AND METHODS, PT 1
Keywords
Field
DocType
fuzzy set,machine learning,indexation
Fuzzy classification,Defuzzification,Fuzzy set operations,Lazy learning,Fuzzy set,Rand index,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy number,Mathematics
Conference
Volume
ISSN
Citations 
80
1865-0929
2
PageRank 
References 
Authors
0.39
11
2
Name
Order
Citations
PageRank
Eva Armengol131532.24
Àngel García-Cerdaña27110.05