Paper Info
Title
FACISME: Fuzzy associative classification using iterative scaling and maximum entropy
Abstract
All associative classifiers developed till now are crisp in nature, and thus use sharp partitioning to transform numerical attributes to binary ones like “Income = [100K and above]”. On the other hand, the novel fuzzy associative classification algorithm called FACISME, which we propose in this paper, uses fuzzy logic to convert numerical attributes to fuzzy attributes, like “Income = High”, thus maintaining the integrity of information conveyed by such numerical attributes. Moreover, FACISME is based on maximum entropy, and uses iterative scaling, both of which lend a very strong theoretical foundation to the algorithm. Entropy is one of the best measures of information, and maximum-entropy-based algorithms do not assume independence of parameters in the classification process. Thus, FACISME provides very good accuracy, and can work with all types of datasets (irrespective of size and type of attributes - numerical or binary) and domains.
Year
DOI
Venue
2010
10.1109/FUZZY.2010.5584127
Fuzzy Systems
Keywords
Field
DocType
data mining,entropy,fuzzy logic,iterative methods,classification process,dataset,fuzzy associative classification algorithm,fuzzy attribute,fuzzy logic,information integrity,iterative scaling,maximum entropy,numerical attribute,parameters independence
Associative property,Pattern recognition,Iterative method,Computer science,Fuzzy logic,Association rule learning,Artificial intelligence,Principle of maximum entropy,Statistical classification,Fuzzy associative matrix,Machine learning,Binary number
Conference
ISSN
ISBN
Citations 
1098-7584
978-1-4244-6919-2
2
PageRank 
References 
Authors
0.38
16
2
Name
Order
Citations
PageRank
Ashish Mangalampalli1294.02
Vikram Pudi231938.97