DocumentCode
2336026
Title
Classification through maximizing density
Author
Wang, Hui ; Düntsch, Ivo ; Bell, David ; Liu, Dayou
Author_Institution
Sch. of Inf. & Software Eng., Ulster Univ., Newtownabbey, UK
fYear
2001
fDate
2001
Firstpage
655
Lastpage
656
Abstract
This paper presents a novel method for classification, which makes use of models built by the lattice machine (LM). The LM approximates data resulting in, as a model of data, a set of hyper tuples that are equilabelled, supported and maximal. The method presented uses the LM model of data to classify new data with a view to maximising the density of the model. Experiments show that this method, when used with the LM, outperforms the C2 algorithm and is comparable to the C5.0 classification algorithm
Keywords
data models; learning (artificial intelligence); pattern classification; classification; data model; density maximization; hyper tuples; lattice machine; Algorithm design and analysis; Computer science; Decision trees; Lattices; Measurement units; Partitioning algorithms; Software engineering; Supervised learning; Tail;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
Conference_Location
San Jose, CA
Print_ISBN
0-7695-1119-8
Type
conf
DOI
10.1109/ICDM.2001.989596
Filename
989596
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