Paper Info
Title
Semi-supervised learning on closed set lattices
Abstract
We propose a new approach for semi-supervised learning using closed set lattices, which have been recently used for frequent pattern mining within the framework of the data analysis technique of Formal Concept Analysis FCA. We present a learning algorithm, called SELF SEmi-supervised Learning via FCA, which performs as a multiclass classifier and a label ranker for mixed-type data containing both discrete and continuous variables, while only few learning algorithms such as the decision tree-based classifier can directly handle mixed-type data. From both labeled and unlabeled data, SELF constructs a closed set lattice, which is a partially ordered set of data clusters with respect to subset inclusion, via FCA together with discretizing continuous variables, followed by learning classification rules through finding maximal clusters on the lattice. Moreover, it can weight each classification rule using the lattice, which gives a partial order of preference over class labels. We illustrate experimentally the competitive performance of SELF in classification and ranking compared to other learning algorithms using UCI datasets.
Year
DOI
Venue
2013
10.3233/IDA-130586
Intell. Data Anal.
Keywords
Field
DocType
semi-supervised learning,data cluster,continuous variable,mixed-type data,formal concept analysis fca,closed set lattice,unlabeled data,self semi-supervised learning,classification rule,data analysis technique,semi supervised learning,formal concept analysis
Decision tree,Classification rule,Stability (learning theory),Semi-supervised learning,Pattern recognition,Computer science,Closed set,Artificial intelligence,Formal concept analysis,Partially ordered set,Machine learning,Learning classifier system
Journal
Volume
Issue
ISSN
17
3
1088-467X
Citations 
PageRank 
References 
1
0.36
22
Authors
2
Name
Order
Citations
PageRank
Mahito Sugiyama17713.27
Akihiro Yamamoto213526.84