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
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;
Conference_Titel :
Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
0-7695-1119-8
DOI :
10.1109/ICDM.2001.989596
